Navigating the messy world of (too many) CVE’s

Introduction to CVE

CVE (Common Vulnerabilities and Exposures) is a database of publicly disclosed security issues. Every vulnerability is uniquely identified by a CVE number & there has been a gradual upward trend in the number of CVEs reported since 1999.

CVE is developed by MITRE corporation. CVE entries are always brief.
CVE is a free service that identifies and lists all known software or firmware vulnerabilities. CVE is not an actionable vulnerability database. It is a standardized dictionary of publicly known vulnerabilities and exposures.

CVE is not a vulnerability database. CVE is created to link vulnerability databases and other capabilities and to facilitate the comparison of security tools and services. CVE does not contain risk, impact, fix information, or detailed technical information. CVE only contains the standard identifier number with a status indicator, a brief description, and references to related vulnerability reports and advisories.

 At this year’s KubeCon + CloudNativeCon America, Pushkar Joglekar, a senior security engineer at VMware Tanzu, spoke about how best to navigate this crisis with a fictitious example.

Communication breakdown?

Starting off with some numbers, the results of a survey towards better understanding Cloud-Native security posture amongst various end-users revealed some surprising statistics. A major part of the problem was that even though ~85% of the surveyed audience reported image scanning as one of the better measures towards security hardening, ~60% of them also reported vulnerability scanning as one of the concern areas in the remit of cloud-native security. This majorly stems from the fact that the results of such scans a.k.a. CVEs, even though documented, are not easily comprehensible to the people at the receiving end of these reports. These results, typically, need to traverse a long chain before being assessed for their severity & impact causing an ultimate delay in the timelines for a typical go-live activity. Speaking about Zero trust & how it is not equal to zero CVE’s, Pushkar dives deep into demystifying some CVEs.

Not just an end user problem?

Shifting our focus for just a bit to the wide range of open source tools & technologies, they obviously are encumbered by these CVEs too. While illustrating this fact, Pushkar takes the example of the Kubernetes project & the efforts it expended in managing and maintaining multiple images. With KEP-1729, the churn on the total number of image versions was aimed to be reduced by rebasing the images to distro less/static from the standard Debian base. However despite these efforts, due to the requirement of iptables the Kube Proxy component continues to use the Debian upstream image & as a result, is also one of the most updated images in the project. Even with this seemingly imperfect solution, the benefits reaped by the Kubernetes project were huge as illustrated by this slide!

How is this relevant?

Circling back to our original fictitious premise, one of the ways to avoid the mammoth efforts undertaken to build, maintain, and update images would be to go down the distro less route. However, when that is not an option, a few ways to alleviate (if not eliminate) the problem would be to

  • Focus on fixable CVEs
  • Understand whether the vulnerabilities were in the code execution path or not
  • Develop better automation towards rebuilding, shipping, & testing of updated base images
  • Create a list of images that require special attention.  

Even when everything’s said and done, vulnerability scanners have their own limitations. Therefore, along with learning about these limitations, it is also extremely essential that we understand the impact of the vulnerabilities reported while simultaneously working towards their remediation.

Remember, it is only through modeling threats and assessing the associated risk that we can manage to be secure despite CVEs. Or as Pushkar would say, manage vulnerabilities by being vulnerable!

Try our new and improved Skedler for custom generated Grafana reports for free!

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Breaking down Elasticsearch in Real-time

There are so many articles on the internet that talk about what elasticsearch is and why you need it. However, when it comes to simplifying the whole elasticsearch/ ELK concept, from a business standpoint, we haven’t really come across a resourceful article or a guide that could help make the buyers’ process easier. This article aims to educate buyers who are considering elasticsearch to understand what it is and why they need it in a simple and jargon-free language.

Heads up!

If you’re a newbie to elasticsearch then you’re just one article away from discovering many of the imperative drivers for using elasticsearch. So, keep reading until the end to know them all.

What’s with elasticsearch? Why are people adopting it widely?

Think of elasticsearch as Google for semi-structured data such as logs. You can simply type in any query to fetch what you want from unstructured or semi structured data. According to Elastic, Elasticsearch is a distributed open-source search and analytics engine for all types of data including textual, numerical, geospatial, structured, and unstructured.

There could be a variety of sources from which you want to collect data such as servers, web servers; network devices such as switches and routers including system metrics, logs and web applications. Before indexing the raw data in Elasticsearch, it needs to be cleaned and enriched after collection. By cleaning and enriching, we mean parsing, normalizing the data before ingestion. Indexing is the process of storing data in elasticsearch in the form of an index where an index is a collection of JSON documents that are correlated to keys ( different field names). Once the data is indexed, users can run complex queries to retrieve large sets of data at an incredibly high speed. The reason as to why Elasticsearch is fast is because, instead of searching the text directly, it searches an index instead. This data can be visualized and viewed on Kibana, which is a data visualization tool to share and manage the dashboards.

How does Elasticsearch help in real-time?

Elasticsearch can be used to index and store all your data logs from a variety of sources. For instance, when a user types in a query in the search tab of an application or applies different filters, the elasticsearch immediately fetches the relevant data and displays it on the frontend tool as a visualization which is called Kibana. Basically, elasticsearch helps in faster retrieval of data.

Now let’s take a look at what makes elasticsearch a favorite tool adopted by a lot of businesses these days.

7 Reasons Why Elasticsearch is Popular among Devops and Secops

#1 . Schema-less. Document Oriented.

Elasticsearch is document-oriented, which does not use schemas and tables to store data. All data is stored in a document form. Therefore, the data is presented in JSON format. So, you can integrate several solutions as it provides you the output in JSON format.

#2. Scalable

It is scalable across multiple nodes. So, you can start with a single node or two or three nodes (for replication) and scale to tens of nodes as your data grows.

#3. Performance

It executes operations on data very fast. It’s incredibly fast relative to other databases like MySQL, RDBMS, etc.

Check elasticsearch vs mongoDB

Check Elasticsearch vs MySQL here

#4. Multilingual

Elasticsearch supports multiple languages.

#5. Autocompletion and Instant Search

Like Google, elasticsearch supports auto-completion and instant search. When you start typing your query, it automatically shows you a dropdown of options to select and complete your query from.

#6. It’s Open Source

Of course, it all comes for free. Elasticsearch is an open-source database search engine. So, anyone can download it without having to pay for the license.

Why Elasticsearch?

Relational Database (RDBMS) works well for structured data such as tabular data of columns and rows, but is not built for semi-structured or unstructured data such as logs and texts. . This also holds true especially when it comes to managing huge data sets, thus leading to slower fetching of results. However, optimizing RDBMS to overcome its limitations also brings in the following set of limitations such as:

  • Every field cannot be indexed
  • Updating rows in heavily indexed tables is a lengthy and exhausting process.

Businesses nowadays are looking for alternate ways where data retrieval is super-fast. This is achievable by adopting NoSQL rather than RDBMS. Elasticsearch is one such NoSQL distributed database. Elasticsearch’s flexible data models help users meet the demanding workload and support low latency required for real-time engagement.

Now that we know why a business needs Elasticsearch, let’s look at one of the most successful precursors that used Elasticsearch to successfully improve their business and whose story would strongly resonate with us all.

How Netflix uses Elasticsearch to improve its messaging and customer operations

Netflix heavily relies on ELK for various use cases to monitor and analyze customer service operations and security logs. The company chose Elasticsearch for its automatic sharding and replication, flexible schema, nice extension model, and ecosystem with many plugins.

In 2007, Netflix expanded its business with the introduction of streaming media. By Jan 2016, Netflix services were available in 190 countries, and in Jan 2017, it reported having around 93 million subscribers worldwide including more than 49 million subscribers just in the United States. With such a humongous user base, Netflix messages millions of customers a day on many channels which includes text messaging, emails, push notification, voice calls, etc.. and these are sent to the customers via their messaging platform which is made up of a series of separate applications.

Wroclaw, Poland – OCT 23, 2019: Man with Netflix logo on screen. Netflix is most popular video streaming platform.

Now let’s uncover how Netflix used elastic stack for higher message deliverability and operational excellence.

Netflix used Elastic stack to monitor and ensure that the messages are delivered to the customers promptly. There were multiple stages involved during each message delivery and hence the message was tracked at each stage.

There was one use case where they had to determine all the countries from which the customers used phone number verification to verify their Netflix accounts. Now, the events triggered for each stage were stored in elasticsearch and they used Kibana to visualize the data. It was found that the UK, Brazil, and the USA has the highest number of customers using the phone number verification method. But considering the success percentage of verification, Brazil had only 70% success rate thus leaving them to ponder the reason for such low percentage. They hence, drilled down the issue to determine the underlying reason. From the analysis, they identified that the users had given an invalid phone number. In the first take, which seemed like nearly 30% of the customers were using an invalid phone number. That’s quite unrealistic. Thus they doubled down on determining the root cause of this problem further.

Theories to low success percentage in phone number verification:

  1. Customers used landline numbers. However, this theory was later dismissed.
  2. The second theory was that, in certain regions of Brazil, the 9th digit prefix to mobile numbers had been mandated to which the users weren’t accustomed as of then. Since the SMS provider in Brazil expected the 9th digit in the phone number, Netflix users were facing an issue in validating their Netflix account with their phone number.
    Thanks to Elastic Stack, it easily pointed out the issue.

One thing to be wary of when considering the ELK stack for your business

Though Elasticsearch is a magical tool that can shoo away all your customer operations and security log woes, remember, great things do come with downsides too. Providing such incredible features for free, elastic doesn’t meet the customers’ needs when it comes to enterprise features such as machine learning, reporting. To use the Kibana reporting option which is in-built in the ELK stack, users need to upgrade to the gold or silver or platinum plans which are priced insanely high.

For this reason, a lot of ELK users end up creating manual reports (also known as copy-pasting dashboard screenshots) thus shelling out around 10-15 hours of manual labor every week ( if they require weekly reports ).

If you can relate to this then you should also check how you can step up from your manual elasticsearch reporting chaos.

On the other hand, some folks are constantly on the lookout for a good Kibana reporting alternative.

PS: We realized that there’s no one go-to article for a potential elasticsearch buyer to learn about elasticsearch reporting during the early stages of their buying process. Hence, we put together Everything you need to know when considering elasticsearch reporting for your business for you to read and learn the ins and outs of elasticsearch reporting from which you can drive actions immediately.

Skedler Vs Kibana Reporting

Skedler Vs Kibana Reporting

Say, you’re an ardent ELK fan for, it’s all open source. But what do you do when your managers, auditors, and customers demand daily/weekly reports? Perhaps you’re researching your elasticsearch reporting options already. If you’re someone who’s looking for a great alternative to Kibana reporting software then keep reading, this one’s for you.

So tracing back the thread, what do you do when your managers, auditors, and customers demand daily/weekly summary of key metrics in their inbox?

While Kibana in its open source version only lets you watch your dashboard visualizations to understand what’s happening in your infrastructure, network, or anything for that matter, would you manually screenshot the dashboards and copy-paste them into a nice report template every day for your stakeholders?

Aaargh! Ouch!

We feel your pain.

To better understand why Kibana reporting is not worth all the hype, we’ve put together a few pointers that could help you compare, evaluate and hence, boil down to a buying decision in a more educated manner.

Now, let’s be brutally honest. Kibana is a great tool! It’s great for developers and analysts. Solves for so many use cases and is so affordable that it comes for free.

Kudos to Kibana!

So, we see, Kibana reports – 1 ; Skedler reports – 0.

However, we also have a lot of folks struggling with Kibana for a handful of reasons. Of all, the preliminary reason behind all those complaints is that the Kibana reporting doesn’t come for free as the dashboard visualization does. Nor is it easy to use. And, speaking of the functionalities, there are quite a few discrepancies that make Kibana reporting a little painful option.

#1. No bloat. Skedler is laser-focused on providing you with the list of highly relevant features requested by you.

We believe a software must be tailor-made to satisfy a customer and his needs. That’s why Skedler lives by the rule of customization.

So, what’s in it for me?

With custom templates, you can send reports to their stakeholders/customers in a more personalized manner. With just a few drag and drops, you can personalize the reports even for each recipient by including brand names, logos, recipients’ names etc.. which allows you to assert your business as a business that’s more concerned with not just delivering data at the right time but also in a credible manner. After all, your reports could become your brand statement. Go make your customers and stakeholders visually fall in love with your super-catchy reports.

This is the reason underpinning for why we give high priority to feature requests made by our customers in both the premium and enterprise-level packages while on a standard level when it comes to the standard edition.

Kibana reports – 0 ; Skedler reports – 1

Skedler is a solution tailor-made for YOUR customer and stakeholder reports.

#2. Rule of thumb (Y): Skedler reporting is a No- Code

First things first. Kibana is a great tool for analysts/developers, but is it really a tool for your managers or customers? Well, I guess they need something simple.

The whole point behind using a frontend tool like Kibana reporting would be to enable anyone to create, generate and fetch reports. In contrast, Kibana doesn’t allow you to enjoy it if you’re a non-techie which makes it a ‘little-not-so-impressive’ solution. But hey, Kibana is ‘open source and all free’. So much that even after upgrading your non-tech teams are still expected to depend on your techies because it’s never code-free.

Skedler is a no-code tool and is extremely easy to use even for the most business users. You don’t have to write any code to integrate Skedler into your Elastic Stack environment and you don’t have to write any code to create custom reports for your managers or customers. You save time, effort and can provide self-service reporting to your stakeholders, all in just a few minutes.

Okay, so that makes Kibana reports – 1 ; Skedler reports – 1.

#3. Elastic simply upsells but do you really want to walk right into it?

If you want to send reports from Kibana in an automated fashion, you are required to upgrade to any of Elastic’s insanely expensive paid plans. But even if you pay BIG $$$$$s per year to purchase the subscription, have you seen the report it produces? It’s a rudimentary screenshot of two charts per page with no customization or features. I wouldn’t blame your managers or customers if they send your reports directly to the trash folder. I’m sure you don’t shell out such huge money for that.

Skedler supports the automated delivery of reports in all four formats — PDF, PNG, HTML, and CSV/Excel. You can create custom templates and send automated reports that your managers and customers would appreciate. All you have to do is configure once and forget forever. The reports reach the right audience at the right time through the right channel.

We provide you with multiple channel options — direct email, slack, or webhook. Additionally, you also get to see the report dashboards inside Skedler whenever you want to check the status of what is sent to whom.

Kibana reports – 0 ; Skedler – 1

Kibana allows you to send bulk automated reports if only you upgrade to Elastic Stack Gold or Platinum subscription.

#4. Reporting costs remain the same even if your business grows

Elastic doesn’t reveal it’s pricing explicitly until you get to a point where you require reports. According to reviews by users, it costs anywhere around $25K – $50K per year to unlock the reporting feature in Kibana.

Skedler’s service packages are more affordable with a fixed unlimited node pricing and we offer much more 1-on-1 support at every package level.

Kibana reports – 0 ; Skedler – 1

Skedler doesn’t let your expenditure go north even when your data and business grows exponentially.

#5. Support from Elastic doesn’t come free – we don’t charge a penny to support you.

Young handsome male customer support phone operator with headset working in his office.

Sometimes, when something goes wrong in your report delivery, we notify your administrator to rectify it. Subsequently, you can reach out to us anytime so that we can figure out things for you within 24 hours. This is an especially useful service because you don’t have to upgrade or burn a hole in your pocket to seek help. With that said, remember you can get technical help via both email, support portal and web conference from us.

Kibana reports – 0 ; Skedler – 1

I’d like to know the different editions available in Skedler

#6. Got Grafana? Skedler’s got you covered.

While Kibana focuses primarily on managing and visualizing logs thus helping you identify and understand all operational and SIEM (Security and Information Event Management) events, you might as well want to incorporate Grafana for your infrastructure monitoring needs. And if you need reporting for Grafana, Grafana Enterprise is neither free nor affordable! On the other hand, Skedler enables you to simply integrate with your ELK stack and Grafana to send the reports you need in a snap.

Kibana reports – 0 ; Skedler – 1

Here’s how you can integrate Grafana with your ELK stack.

Key Takeaways:

  1. Your reporting solution needs to be easy to use, scalable, and most importantly, affordable. Anything lesser than or beyond this is either not worth your time or is just overwhelming for your business.
  2. Look for a reporting solution that offers exactly what you need and something that can be adopted unanimously. You can expect a reporting solution that allows you to:
    • Customize your own report layout
    • Create your own templates
    • Personalize reports for each user
    • Create reports from search, data tables
  3. Extending support to your business is something that goes without saying. Your reporting solution provider is bound to do that for free and make sure you don’t burn your pocket for it.

Hey, and the final scores ?

Isn’t that quite obvious? 😉

If you would like to personally check the scores, sign up for a free trial and see for yourself.

Manual Elastic Search Reporting Alternative

Here’s how you can relate and step up from your manual elasticsearch reporting chaos

“You have all the time in the world to fetch reports”, said nobody ever.

The Techie in you
  • #1 — Screenshots Kibana / Grafana dashboards
  • #2 — Copy-pastes it on a blank document using a template carefully
  • #3 —  Creates exclusive documents for each customer based on their requirements painstakingly
  • #4 — Attaches them on an email manually
  • #5 — Sends it at the right time. Shuts the laptop. Takes the biweekly headache pill and crashes.

Sounds familiar?

Okay, so that’s exactly what you do when your manager, stakeholders or your customers ask you to send a quick report especially when you don’t have a structured approach to reporting.

Well… Been there, Done That.

Developers are not meant to do repetitive but innovative tasks

Copy pasting screenshots of dashboards is not even the last thing you want to do. On average, assume, you spend upto one hour screenshotting and copy-pasting dashboards to generate a single report. Imagine having to create ten such reports every week. It’s worth noting that you might have to spend 10 hours a week.

That’s expensive. 

Now that brings us to automation. Let’s talk.

What We Mean When We Talk About Automated Reports

Automated reports are the reports that get generated, scheduled and delivered with zero manual intervention. For instance, we have Skedler which is one such out of the box elasticsearch reporting solution that’s renowned for its tailor-made features & functionalities and ease of use. The underlying challenge with finding a Kibana reporting alternative is its popularity. In fact, the popularity of Kibana has undermined the need for reporting. It’s later when Elasticsearch users realize that they need an elasticsearch reporting solution to analyse their data methodically.

A workplace does not simply start using Skedler. It is not “adopted” in the manner of a new system for payroll, or a new video meeting app. Skedler arrives like a word of a new elasticsearch reporting software in your tech stack or a coming efficient software to collaborate with customers and stakeholders on what matters the most — data. Skedler is where and how the reporting monotony is killed and time saved.

For engineering teams, Skedler is a gamechanger. It’s used to keep track of various operational, business, and security intelligence metrics on a high level. In banks and other financial sectors, Skedler helps folks in fraud monitoring, transaction monitoring, and reporting. 

Skedler wants to replace manual reporting

What substantiates the fact that Skedler replaces manual reporting is its quickness. The faster you get things done, the better.

But, Is Faster Better?

Manual reporting is bad, and so therefore it must follow that Skedler is good. Skedler eliminates copy-pasting screenshots, manual report scheduling and delivering. 

Among the small but meaningful technical features of Skedler is the ability it provides, by default, to any user to view reports. An office with a vibrant tech-savvy culture might spawn long screenshotting, copy-pasting dashboards, or manually email groups of different customers different reports, but Skedler’s tendency to support endless burst filters, with varying degrees of perceived conditions, has fostered great results. Customers have used Skedler to build super-intuitive infographics which give rise to pristine data representation and hence better decisions. Sometimes these infographics are used in sales and other operational discussions, while others are used in reports and are surely shared across either with customers or stakeholders.

4 ways how Skedler makes reporting easier 

#1 —- Reports organized methodically

 Instead of an overstuffed dashboard and folders with cluttered screenshots, elasticsearch reports are stored and available in dashboards for a quick glance.

 #2 —- Spend absolutely no time copy-pasting

You could get buried in screenshots or you can get your metrics at your fingertips with Skedler’s automated reports.

#3 —- Design reports faster

You don’t have to get overwhelmed when it comes to designing your reports. Skedler spoils you with palatable pre-built templates or you can just customize one for yourself. 

 #4 —- Stay informed, Stay aware

Skedler makes it simple to follow what reports are sent to whom, when, and what channel on the fly.

If you’re looking to step up from your chaotic / overwhelming manual elasticsearch reporting process then try Skedler.

Don’t believe in us. Opt for free trial and see how automated elasticsearch reports can change your life for yourself!

Elastic Search Reporting For Your Business

What is elasticsearch reporting?

Elasticsearch reporting tools are also called elasticsearch report generators that help in fetching visualization from the ELK stack and Kibana dashboards and converting them into easily-decipherable reports.

Elasticsearch reporting is the easiest and quickest way to deliver reports to your teammates, customers and customers of customers

Simply put, elasticsearch Reporting is used to analyze and derive insights out of your elasticsearch data visualizations such as Kibana or Grafana. Elasticsearch reports are used in different segments of a business to analyze, gain insights, and make decisions. It helps in communicating your value to your customers thus fostering and maintaining a good relationship with them. Internally, an elasticsearch reporting tool is used for various reporting purposes such as operational intelligence reporting, security intelligence, business intelligence, infrastructure monitoring, fraud monitoring, etc.The goal is simple: Allow users to quickly access the elasticsearch data without having to stare at their dashboards perennially. These reports also come across as a value add to a lot of businesses these days to meet SLAs which in turn leads to happy customers.

How Elasticsearch reporting is changing the way teams perceive data?

There’s a problem with the way we see reports. We think visualizations can replace reports. But in reality, it’s far different.Think about it — what do you do when you have to quickly run for an impromptu meeting with your team on the last month’s data? Or even more specific metrics? For most people, it looks like they have to sit and write some super-boring code to fetch that report.

But what if we told you there’s a 10x better way to do it without any manual intervention?

People love using automated reporting tools because it’s fast, easy and requires no manual intervention. And this means more time for other appropriate things in life.

But most businesses are forcing teams to develop, design and deliver elastic search reports manually. Now this sure is a herculean task for sectors like the MSPs and MSSPs when they have to churn reports in bulk. And most importantly, you need to make the reports appear more personal.

Your customers, teammates and everyone is curious. With elasticsearch transforming the whole data analytics game, all of them turn data-curious and want to make informed decisions.

Purpose of an Elasticsearch Reporting tool

Different teams require different reports. For example, your sales team requires a different set of reports at a different juncture when compared to your Secops team or any other team for that matter. You struggle to create, schedule, and deliver reports to different teams at different times.Elasticsearch reporting helps in breaking down your simple visualization from Kibana( or Grafana) to simpler reports so that anyone from any team can simply receive their reports on their email or any other preferred channel such as Slack.

Automated reporting

With an automated reporting solution, you can configure your reports once and forget it forever. Your recipients would receive the right reports at the right time on the right channel.

Elasticsearch report scheduling

Automating also means delivering the right information at the right time. Scheduled reports cut the need for manual intervention and allow you to schedule the reports at your convenience. Who cares about time zones anymore?

Role Based Access Control

Elasticsearch reporting tools allow you to provide users with the right level of access based on their role. They can have access to generate reports or they can just view them. You decide.

Data Privacy

Your data is your wealth. If you are a company that holds sensitive information about your clients, you don’t want to reveal your entire dashboard when certain customers are requesting you for reports. Using an elasticsearch reporting tool you can simply send out only the required fields of your report.

Alerts with reports

You can integrate your elasticsearch reporting solution with a smart elastic alerting solution to detect anomalies and notify you on your preferred channel with appropriate reports.

Bid goodbye to unsatisfied stakeholders/customers

Do your stakeholders/customers grow a little frustrated if you can’t render the reports that they ask for? Satisfy them with the most valuable reports and go above & beyond in meeting your SLAs like never before.

Instead of forcing your DevOps or other techie friends to create reports manually when your manager wants to run a quick stand-up meeting, elasticsearch reporting tools help you schedule and deliver the right set of reports to the right recipient at the right time and most importantly on the right Channel

Who needs an Elasticsearch Reporting tool?

An elasticsearch reporting tool gives everyone — from SecOps, DevOps, sales, customer service, marketing, MSSPs, MSPs, or any other line of business — a better way to fetch, schedule and, deliver elasticsearch reports to customers, stakeholders, and team members to drive success. An elasticsearch reporting tool lets you create and design infographic-like reports based on your choice.

With reporting automation, it’s easier to schedule and deliver reports on your preferred channels and hence increase productivity. Everyone in your company can look into the reports and make informed decisions. Elasticsearch reporting can help companies of all sizes drive business growth, and it can be especially beneficial to a small organization, where teams often need to find ways to do more with less.

Whether you are a startup, a small business, or an enterprise, using Kibana or Grafana as your data visualization tool, an elasticsearch reporting tool allows you to create, design, schedule, and deliver elasticsearch reports on the go. It helps you meet your SLAs, derive great insights from your elasticsearch data, and deliver the right information at the right time to the right recipient on the right channel thus satisfying your stakeholders and customers.

The best part?

You can automate all of this on the fly.

Here’s how different business functions can benefit from using an elasticsearch reporting tool.

Based on company size


As a start-up, you might want to carefully evaluate every possible solution that can fit into both your budget and environment. There are only a handful of good elasticsearch reporting solutions that you can count on.

While choosing an elasticsearch reporting software for start-ups, be careful about your evaluation procedure.

Small businesses

As a small business, you can’t afford to spend like the enterprises on the Elasticsearch reporting that is available only with Elastic’s Gold or Platinum level subscription . If your existing elasticsearch reporting solution isn’t helping, you can take a look at the challenges with your existing elasticsearch reporting tool and compare it with ones available in the market.

Make sure your potential elasticsearch reporting tool is something that fits into your budget and requirement as well.


You’re always handling large sets of data from multiple data sources and are looking to derive great insights from your data, supporting customers with the data they need on a regular basis, and so on. This means a lot of teams across your business are looking at reports as their holy grail—sales, DevOps, secops, customer success, etc.. Subsequently you don’t want your technical teams to subordinate their tasks to writing some super-boring code. An elasticsearch reporting software is indispensable for any enterprise that wants to automate such dull and mundane reporting tasks.

By use case

Operational Reporting

Operational reporting helps managers and stakeholders to readily understand and analyze the operational behavior inside an organization. It covers different aspects of business like tracking sales performance to process improvement to manufacturing productivity and so on and so forth.

While operational reporting is an umbrella term, there are a plethora of other types of data that the report can carry based on the industry.

Fraud Monitoring

Your fraud monitoring system is fast and so should your reporting stack be. If you’re in the banking or the insurance sector you probably know how imperative it is to deploy a fraud detection system. Increase operational efficiency by monitoring fraud investigation efforts, cash transaction monitoring, billing, check tampering, and stay ahead of transactional risks forever.

Infrastructure Monitoring

Figuring out the hardware’s physical health, operating system’s utilization and depletion, your network’s bandwidth consumption and errors are just some of the uses of a reporting tool. These reports help in putting together a stripped-down version of your complex key analytics in front of you and the decision-makers.

Across business types


As an MSSP you might have to provide and manage various components of security technologies such as data protection, patch management, intrusion detection, privileged access management, and so on. You also want to ensure that enterprise IT is always up-to-date with the status of security issues, audits, and maintenance, enabling the hiring organization to focus on security governance rather than administrative tasks. Don’t let your clients only hear from you in times of crisis and security incidents. Talk to them and keep them informed as frequently as possible. This also allows you to showcase your value to your customers.


As an MSP you might be responsible for sending reports based on various aspects such as mobile device management, asset reports, log-on/ log-off reports, etc. These reports allow you to learn the whereabouts of assets, users, any suspicious activity inside the network, reduce overhead expenses, etc..Leveraging elasticsearch reporting can help you foster that business value among your customers.

Telecom Service Providers

Category wise growth in subscriber base, growth in wireless subscribers, area wise statuses are some of the reports you would want to comb through on a weekly, monthly, and yearly basis. With the right elasticsearch reporting tool in place such reports come in handy when you want to quickly prep yourself up for a sales presentation.

Financial Services

To keep your finances flowing across the organization you have to care about certain KPIs. Monthly financial reports which include transactions, cash flow, accounts payable turnover ratio, Profit & Loss financial report analysis, Financial performance, Financial statements, measure revenue loss and receivables, etc..

If you’re a business that’s looking to implement elasticsearch reporting but are stuck somewhere amidst the process, we’re here to help! We’ll get in touch with you soon. Meanwhile you can find answers to some of the most basic yet imperative questions that every elasticsearch user has at this juncture here.

When do I need an Elasticsearch reporting software?

You might need an elasticsearch reporting software if:

  1. You are a business that just got started with using Elasticsearch and are wondering how to fetch elasticsearch reports without shelling out money on the expensive Kibana reporting.
  2. You are already using the ELK stack, which includes Kibana reporting / Kibana reporting plugin to fetch elasticsearch reports for you. But you’re planning to switch from Kibana reporting to an even more affordable elasticsearch reporting tool alternative. In this case, you may want to take a look at the various other options available in the market.

Well, it’s likely that no one would want to upgrade plans or invest insane amounts of money just to export their elasticsearch reports into PDFs, CSVs, or any other readable formats.

Choosing the right ES reporting tool for your business

There are a number of factors you want to consider before boiling down to an Elasticsearch reporting tool. The best way to choose one is to first identify your challenges with or without your existing solution. Some of the important factors you want to consider:

  1. Price
  2. Simple Intuitive Interface
  3. Ease of integration
  4. Deployment Flexibility — Cloud or On-premise
  5. Multi-channel support

A lot of times these are the reports that you want to present to your stakeholders or customers. Hence it is imperative that you look out for an infographic-like reporting tool that can enhance the appearance of your reports largely.

Essential Features in an ES reporting tool

  1. Automated reporting
  2. Elasticsearch report schedulingCustomizable reports
  3. Customizable reports
  4. Multi-channel report delivery

How to implement an ES reporting tool

Define your use case

Before you plan on implementing a software solution to fix any of your problems remember to define your situation that pushed you to take this step. Read up some case studies or customer testimonials to better understand if the solution that you’re evaluating is the right one for your business.

See how the elasticsearch reporting tool can fit your business needs

If PDF reports are your holy grail, your elasticsearch reporting software should be capable of seamlessly converting your kibana/grafana visualization into a PDF. If Slack is the most preferred medium by your customers, make sure your elasticsearch report generator is capable of delivering the scheduled reports on the same channel.

Set-up goals

If you want to meet your SLAs by sending out a certain number of reports, make sure to tie this back to the kind of subscription plan you are going to adopt with your elasticsearch report. Ensure the upgrading or the upsizing process is hassle-free.

Help your team members pull or configure reports whenever you need them

The whole point of employing an elasticsearch reporting software is to ensure that anyone from your team can just schedule the reports and ensure delivery without having to code. Hence, don’t forget to choose an elasticsearch reporting solution that has zero dependencies on code.

Running a business without elasticsearch reporting costs you real customer satisfaction

More manual intervention means less time for everything else. An active DevOps team can generate a flood of data. Devs are out on the road analyzing data, connecting the dots, looking to find patterns, and remediate all the roadblocks for a seamless operation – but all too often a lot of information gets stored in visualization, handwritten notes, or inside the heads of your team members.

Details can get lost, metrics are not followed up on promptly, clients could ask for on-demand reports. And it can all be compounded if your DevOps team cannot find the crux of a bunch of data when needed. But it’s not just DevOps that suffer without elasticsearch reporting.

Reports can be hard to create and they can waste valuable innovating time. Customers can lose sight of what their systems are up to, managers can find it hard to create reports manually and the technical teams could be really busy. Which means that they can’t offer the right support at the right time.

Even if you do successfully collect all the data and create reports manually, you’re faced with the challenge of delivering it promptly. It can be difficult to deliver the reports at the right time to the right audience on the right channel.

Your customers/organization may be using a range of different platforms including email, Slack, or teams — to interact with their customers and teams internally. They might not have all the time in the world to sit and stare at their elasticsearch dashboards to analyze and comprehend what’s happening. It’s imperative to have your elasticsearch reporting tool support report delivery on their preferred channels so they don’t have to switch tabs every time they want to learn the scene.

6 ways how an elasticsearch reporting tool can help your business today.

  1. Build a better relationship with your stakeholders
  2. Meet SLAs promptly.
  3. For users who don’t have direct access to dashboards or analytic tools, reports a major source of data.
  4. Drive day to day operations
  5. Zero manual intervention — Save time for your ops team
  6. Creates an opportunity for your stakeholders and C-level executives to understand the operational data at a high level

Struggling with an inconsistent elasticsearch reporting process?

To understand visualization cohesively, we need reports with which you or your customers/stakeholders will be able to form cohorts of the elasticsearch data. An elasticsearch reporting solution will help in maintaining the consistency of delivering reports in an automated fashion.

Skedler Reports v4.19.0 & Alerts v4.9.0 now supports ELK 7.10

Here are the highlights of what’s new and improved in Skedler Reports 4.19.0 & Alerts 4.9.0. For detailed information about this release, check the release notes.

Indexing speed improvement

Elasticsearch 7.10 improves indexing speed by up to 20%. We’ve reduced the coordination needed to add entries to the transaction log. This reduction allows for more concurrency and increases the transaction log buffer size from 8KB to 1MB. However, performance gains are lower for full-text search and other analysis-intensive use cases. The heavier the indexing chain, the lower the gains, so indexing chains that involve many fields, ingest pipelines or full-text indexing will see lower gains which can now be utilized in Skedler v4.19.0.

More space-efficient indices

Elasticsearch 7.10 depends on Apache Lucene 8.7, which introduces higher compression of stored fields, the part of the index that notably stores the _source. On the various data sets that we benchmark against, we noticed space reductions between 0% and 10%. This change especially helps on data sets that have lots of redundant data across documents, which is typically the case of the documents that are produced by our Observability solutions, which repeat metadata about the host that produced the data on every document.

Elasticsearch offers the ability to configure the index.codec setting to tell Elasticsearch how aggressively to compress stored fields. Both supported values default and best_compression will get better compression with this change.

Data tiers

7.10 introduces the concept of formalized data tiers within Elasticsearch. Data tiers are a simple, integrated approach that gives users control over-optimizing for cost, performance, and breadth/depth of data. Prior to this formalization, many users configured their own tier topology using custom node attributes as well as using ILM to manage the lifecycle and location of data within a cluster.

With this formalization, data tiers (content, hot, warm, and cold) can be explicitly configured using node roles, and indices can be configured to be allocated within a specific tier using index-level data tier allocation filtering. ILM will make use of these tiers to automatically migrate data between nodes as an index goes through the phases of its lifecycle.

Newly created indices abstracted by a data stream will be allocated to the data_hot tier automatically, while standalone indices will be allocated to the data_content tier automatically. Nodes with the pre-existing data role are considered to be part of all tiers.

AUC ROC evaluation metrics for classification analysis

The area under the curve of the receiver operating characteristic (AUC ROC) is an evaluation metric that has been available for outlier detection since 7.3 and now is available for classification analysis. AUC ROC represents the performance of the classification process at different predicted probability thresholds. The true positive rate for a specific class is compared against the rate of all the other classes combined at the different threshold levels to create the curve.

Custom feature processors in data frame analytics

Feature processors enable you to extract process features from document fields. You can use these features in model training and model deployment. Custom feature processors provide a mechanism to create features that can be used at search and ingest time and they don’t take up space in the index. This process more tightly couples feature generation with the resulting model. The result is simplified model management as both the features and the model can easily follow the same life cycle.

Points in time (PITs) for search

In 7.10, Elasticsearch introduces points in time (PITs), a lightweight way to preserve index state over searches. PITs improve the end-user experience by making UIs more reactive supported by Skedler v4.19.0

By default, a search request waits for complete results before returning a response. For example, a search that retrieves top hits and aggregations returns a response only after both top hits and aggregations are computed. However, aggregations are usually slower and more expensive to compute than top hits. Instead of sending a combined request, you can send two separate requests: one for top hits and another one for aggregations. With separate search requests, a UI can display top hits as soon as they’re available and display aggregation data after the slower aggregation request completes. You can use a PIT to ensure both search requests run on the same data and index state.

New thread pools for system indices

We’ve added two new thread pools for system indices: system_read and system_write. These thread pools ensure system indices critical to the Elastic Stack, such as those used by security or Kibana, remain responsive when a cluster is under heavy query or indexing load.

system_read is a fixed thread pool used to manage resources for reading operations targeting system indices. Similarly, system_write is a fixed thread pool used to manage resources for write operations targeting system indices. Both have a maximum number of threads equal to 5 or half of the available processors, whichever is smaller.

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Kibana Single Sign-On with OpenId Connect and Azure Active Directory


Open distro supports OpenID so you can seamlessly connect your Elasticsearch cluster with Identity Providers like Azure AD, Keycloak, Auth0, or Okta. To set up OpenID support, you just need to point Open distro to the metadata endpoint of your provider, and all relevant configuration information is imported automatically. In this article, we will implement a complete OpenID Connect setup including Open distro for Kibana Single Sign-On.

What is OpenID Connect?

OpenID Connect 1.0 is a simple identity layer on top of the OAuth 2.0 protocol. It allows Clients to verify the identity of the End-User based on the authentication performed by an Authorization Server, as well as to obtain basic profile information about the End-User in an interoperable and REST-like manner.

OpenID Connect allows clients of all types, including Web-based, mobile, and JavaScript clients, to request and receive information about authenticated sessions and end-users. The specification suite is extensible, allowing participants to use optional features such as encryption of identity data, the discovery of OpenID Providers, and session management, when it makes sense for them.

Configuring OpenID Connect in Azure AD

Next, we will set up an OpenID Connect client application in Azure AD which we will later use for Open Distro for Elasticsearch Kibana Single Sign-On. In this post, we will just describe the basic steps.

Adding an OpenID Connect client application

Our first step is, we need to register an application with the Microsoft identity platform that supports OpenID Connect. Please refer to the official documentation.

Login to azure ad and open the Authentication tab in-app registrations and enter the redirect URL as https://localhost:5601/auth/openid/login and save it.

redirect URL – https://localhost:5601/auth/openid/login

Besides the client ID, we also need the client secret in our Open Distro for elasticsearch Kibana configuration. This is an extra layer of security. An application can only obtain an id token from the IdP if it provides the client secret. In Azure AD you can find it under the Certificates & secrets tab of the client settings.

Connecting OpenDistro with Azure AD

For connecting Open Distro with Azure AD we need to set up a new authentication domain with type openid in config.yml. The most important information we need to provide is the Metadata Endpoint of the newly created OpenID connect client. This endpoint provides all configuration settings that Open Distro needs. The URL of this endpoint varies from IdP to IdP. In Azure AD the format is:

openId end point IDP –

Since we want to connect Open Distro for Elasticsearch Kibana with Azure AD, we also add a second authentication domain which will use the internal user database. This is required for authenticating the internal Kibana server user. Our config.yml file now looks like:



              http_enabled: true 

              transport_enabled: true 

              order: 0 


                 type: “basic” 

                 challenge: false 


                 type: “internal” 


              enabled: true 

              order: 1 


                 type: openid 

                 challenge: false 


                     subject_key: preferred_username 

                     roles_key: roles 




                   type: noop

Adding users and roles to Azure AD

While an IDP can be used as a federation service to pull in user information from different sources such as LDAP, in this example we use the built-in user management. We have two choices when mapping the Azure AD users to Open Distro roles. We can do it by username, or by the roles in Azure AD. While mapping users by name is a bit easier to set up, we will use the Azure AD roles here.

With the default configuration, two appRoles are created, skedler_role and guidanz_role, which can be viewed by choosing the App registrations menu item within the Azure Active Directory blade, selecting the Enterprise application in question, and clicking the Manifest button

A manifest is a JSON object that looks similar to:

“appId”: “xxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxx”,

“appRoles”: [


   “allowedMemberTypes”: [



   “description”: “Skedler with administrator access”,

   “displayName”: “skedler_role”,

   “id”: “xxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxx”,

   “isEnabled”: true,

   “value”: “skedlerrole”



   “allowedMemberTypes”: [



   “description”: “guidanz with readonly access”,

   “displayName”: “guidanz_role”,

   “id”: “xxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxx”,

   “isEnabled”: true,   

   “value”: “guidanzrole”


         ], … etc.  


There are many different ways we might decide to map how users within AAD will be assigned roles within Elasticsearch, for example, using the tenantid claim to map users in different directories to different roles, using the domain part of the name claim, etc.

With the role OpenID connect token attribute created earlier, however, the appRole to which an AAD user is assigned will be sent as the value of the Role Claim within the OpenID connect token, allowing:

  • Arbitrary appRoles to be defined within the manifest
  • Assigning users within the Enterprise application to these roles
  • Using the Role Claim sent within the SAML token to determine access within Elasticsearch.

For the purposes of this post, let’s define a Superuser role within the appRoles:


  “appId”: “<guid>”,

  “appRoles”: [


      “allowedMemberTypes”: [



      “displayName”: “Superuser”,

      “id”: “18d14569-c3bd-439b-9a66-3a2aee01d14d”,

      “isEnabled”: true,

      “description”: “Superuser with administrator access”,

      “value”: “superuser”


    … other roles


  … etc.

And save the changes to the manifest:

Configuring OpenID Connect in Open Distro for Kibana

The last part is to configure OpenID Connect in Open Distro for Kibana. Configuring the Kibana plugin is straight-forward: Choose OpenID as the authentication type, and provide the Azure AD metadata URL, the client name, and the client secret. Please refer to the official documentation.

Activate OpenID Connect by adding the following to kibana.yml:

opendistro_security.auth.type: “openid”

opendistro_security.openid.connect_url: “”

opendistro_security.openid.client_id: “xxxxx-xxxx-xxxx-xxxx-xxxxxxxxx” 

opendistro_security.openid.client_secret: “xxxxxxxxxxxxxxxxxxxxxxxxxxx”

opendistro_security.openid.base_redirect_url: “https://localhost:5601”

Done. We can now start Open Distro for Kibana and enjoy Single Sign-On with Azure AD! If we open Kibana, we get redirected to the login page of Azure AD. After providing username and password, Kibana opens, and we’re logged in.


OpenID Connect is an industry-standard for providing authentication information. Open Distro for Elasticsearch and their Open Distro for Kibana plugin support OpenID Connect out of the box, so you can use any OpenID compliant identity provider to implement Single Sign-On in Kibana. These IdPs include Azure AD, Keycloak, Okta, Auth0, Connect2ID, or Salesforce.


If you wish to have an automated reporting application, we recommend downloading  Skedler Reports.

The Best Tools for Exporting data from Grafana

As a tool for visualizing data for time series databases, Logging & document databases, SQL, and Cloud, Grafana is a perfect choice. Its UI interface allows creating a dashboard and visualizations in minutes and analyzing the data with its help.

Despite having tons of visualizations, the open-source version of Grafana does not have advanced reporting capability. Automating the export of data into CSV, Excel, or PDF requires additional plugins.

We wrote an honest and unbiased review of the following tools that are available for exporting data directly from Grafana.

  1. Grafana reporter
  2. Grafana Data Exporter
  3. Skedler Reports

1. Grafana Reporter

A simple Http service that generates *.PDF reports from Grafana dashboards.

Runtime requirements

  • pdflatex installed and available in PATH.
  • a running Grafana instance that it can connect to. If you are using an old Grafana (version < v5.0), see Deprecated Endpoint below.

Build requirements:

  • Golang

Pros of Grafana Reporter

  • Simply embeddable tool for Grafana
  • Uses simple curl commands and arguments

Cons of Grafana Reporter

  • You need pdflatex and Golang. So you must install a Golang environment in your system.
  • For non-technical users, it’s difficult to use
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2. Grafana Data Exporter

Server for fetching data from Grafana data sources. You would you use it:

  • To export your metrics on a big range
  • To migrate from one data source to another

Runtime requirements

  • Linux.
  • Docker


  • grafana-data-exporter for fetching data from Grafana data sources.
  • Simple-JSON-data source for progress tracking.
  • grafana-data-exporter-panel for exporting metrics from the dashboard.
  • Import exporting template dashboard at http://<YOUR_GRAFANA_URL>/dashboard/import.

Pros of Grafana Data Exporter

  • Faster writing of documents
  • Added as a Grafana panel

Cons of Grafana Data Exporter

  • To automate the exporting of data on a periodic basis, you need to write your own cron job
  • Grafana Data Exporter installation is a bit tricky for non-technical users

3. Skedler Reports

Disclosure: Skedler Reports is one of our products.

Skedler offers a simple and easy to add reporting and alerting solution for Elastic Stack and Grafana. There is also a plugin for Kibana that is easy to install and use with the Elasticsearch data. It’s called Skedler Reports as Kibana Plugin.

Pros of Skedler Reports

  • Simple to install, configure, and use
  • Send HTML, PDF, XLS, CSV reports on-demand or periodically via email or #slack
  • Report setup takes less than 5 minute
  • Easy to use, no coding required

Cons of Skedler Reports

  • It requires a paid license which includes software and also enterprise support
  • Installation is difficult for users who are not fully familiar with Elastic Stack or Grafana
Schedule & Automate Your Grafana Reports Free with Skedler. Fully featured free trial.

What tools do you use? 

Do you have to regularly export data from Grafana for external analysis or reporting purposes?  Do you use any other third-party tools? Email us about the tool at hello at

The Best Tools for Exporting Elasticsearch Data from Kibana

As a tool for visualizing elasticsearch data, Kibana is a perfect choice. Its UI interface allows creating a dashboard, search, and visualizations in minutes and analyzing the data with its help.

Despite having tons of visualizations, the open source version of Kibana does not have advanced reporting capability. Automating export of data into CSV, Excel, or PDF requires additional plugins.  

We wrote an honest and unbiased review of the following tools that are available for exporting data directly from Elasticsearch.

  1. Flexmonster Pivot plugin for Kibana 
  2. Sentinl (for Kibana)
  3. Skedler Reports

1. Flexmonster Pivot plugin for Kibana

Flexmonster Pivot covers the need in summarizing business data and displaying results in a cross-table format interactively & fast. All these Excel-like features, to which so many of you are used to, and its extended API will multiply your analytics results remarkably.

Though initially created as a pivot table component that can be incorporated into any app that uses JavaScript, it can serve as a part of Kibana as well. You can connect it to the Elasticsearch index, fetch the documents from it and start exploring the data.

Pros of Flexmonster Pivot plugin for Kibana

  • Flexmonster is in line with the concept of Kibana
  • Simply embeddable Pivot for Kibana

Cons of Flexmonster Pivot plugin for Kibana

  • To automate the exporting of data on a periodic basis, you need to write your own cron job.
  • Flexmonster Pivot plugin installation is a bit tricky. 

2. Sentinl (for Kibana)

SENTINL extends Kibana with Alerting and Reporting functionality to monitor, notify and report on data series changes using standard queries, programmable validators and a variety of configurable actions – Think of it as a free and independent “Watcher” which also has scheduled “Reporting”.

SENTINL is also designed to simplify the process of creating and managing alerts and reports in Siren Investigate/Kibana 6.x via its native App Interface, or by using native watcher tools in Kibana 6.x+.

Pros of Sentinl

  • It’s simple to install and configure
  • Added as a Kibana plugin.

Cons of Sentinl

  • This tool supports only 6x versions of Elasticsearch.  It does not support 7.x.
  • For non-technical users, it’s difficult to use 
  • Automation requires scripting which makes it laborious

3. Skedler Reports

Disclosure: Skedler Reports is one of our products.

Skedler offers a simple and easy to add reporting and alerting solution for Elastic Stack and Grafana.  There is also a plugin for Kibana that is easy to install and use with the Elasticsearch data. It’s called Skedler Reports as Kibana Plugin. 

Pros of Skedler Reports

  • Simple to install, configure, and use
  • Send HTML, PDF, XLS, CSV reports on-demand or periodically via email or #slack
  • Report setup takes less than 5 minute
  • Easy to use, no coding required

Cons of Skedler Reports

  • It requires a paid license which includes software and also enterprise support
  • Installation is difficult for users who are not fully familiar with Elastic Stack or Grafana

What tools do you use?

Do you have to regularly export data from Kibana for external analysis or reporting purposes? Do you use any other third-party plugins?   Email us about the tool at hello at

The Best Tools for Exporting Elasticsearch Data to CSV


This blog post shows you how to export data from Elasticsearch to a CSV file. Imagine that you have infrastructure or security log data in Elasticsearch that you would like to export as a CSV and open in Excel or other tools for further analysis. In this post, we’ll introduce the ways to export Elasticsearch data to a CSV using the top available tools on the market.

Possible Scenarios

There are multiple ways you can extract data from Elasticsearch. We will look at the following scenarios:

Export data directly from Elasticsearch

We wrote an honest and unbiased review of the following tools that are available for exporting data directly from Elasticsearch.

  1. Es2csv – A CLI tool for exporting data from Elasticsearch to a CSV file
  2. Python pandas – A python software library that has built in functions for exporting elasticsearch data in a CSV, Excel or HTML format.
  3. Elasticsearch Data Format Plugin

1. es2csv

Es2csv is the command-line utility, written in Python, for querying Elasticsearch in Lucene query syntax or Query DSL syntax and exporting the results as documents into a CSV file. This tool can query bulk docs in multiple indices and get only selected fields, which reduces query execution time.

Here are the major pros and cons of es2csv :

Pros of es2csv

Here are the most essential advantages of es2csv.

  • It’s simple to install and configure
  • This tool can query bulk docs in multiple indices and get only selected fields.
  • It reduces query execution time.

Cons of es2csv

After the advantages, it’s time to throw some light on the disadvantages of es2csv.

  • This tool supports only 2x and 5x versions of Elasticsearch. It does not support 6.x or 7.x
  • You need Python 2.7.x and pip. So you must install a python environment in your system.
  • For non-technical users it’s difficult to use
  • To automate the exporting of data on a periodic basis, you need to write your own cron job.

2. Python-pandas

One of the advantages of having a flexible database and using Python’s Pandas Series is being able to export documents in a variety of formats. When you use Pandas IO Tools Elasticsearch to export Elasticsearch files in Python, you can analyze documents faster.
This requires the following prerequisites

  1. Install Python
  2. Install pip
  3. Pip install elasticsearch
  4. Pip install numpy
  5. Pip install pandas

Here are the major pros and cons of python pandas.

Pros of python pandas

  • Faster writing of documents
  • Since it is written in python, when we analyze it in terms of code level, the amount of code is less when compared to the amount of code written in nodejs
  • Supports elasticsearch version 7.x as well

Cons of Python pandas

  • Python needs to be installed properly.
  • Not able to export values with queries.
  • Automation requires scripting
  • It is a tool for developers and data scientists, not for non-technical users.

3. Elasticsearch Data format

This is an elastic search plugin. You need to add and configure it into your elasticsearch plugins. It provides a feature to download the response of a search result in several formats other than JSON. The supported formats are CSV, Excel and JSON(Bulk).
For this, there are the following prerequisites

  1. Elastic search 5.X or below
  2. Java installed and JAVA_HOME path configured

Here are the major pros and cons of the Elasticsearch Data format.

Pros of Elasticsearch Data format

  • Easy to install.
  • Added as an elastic search plugin.
  • Uses simple curl commands and arguments.

Cons of Elasticsearch Data format

  • The response format is poor.
  • Supports only up to elasticsearch 5.x.
  • Difficult to use for non-technical users

Overall Summary

When we tried these tools, we were unable to export only the fields that are given in the query. So it takes all the values in the index. Whereas, with products like Skedler Reports, Kibana, and Grafana, it is possible to export the selected fields as a CSV/Excel file. Furthermore, only python pandas works with the latest versions of elasticsearch(>5.x). Last, but not least, a major drawback of these open source tools is that they are designed for use by technical users.

If you are looking for an easy and automated way to export Elastic Stack data to CSV, XLS or PDF, we invite you to try Skedler Reports. It is free to try and it could save you a ton of time.

If you are looking for export Elastic Stack data to CSV, XLS or PDF, be sure to test drive Skedler.

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