Everything You Need to know about Data Observability

Data derived from multiple sources in your arsenal affects the business decision. It may be data that your marketing team needs or you need to share some statistics with your customer; you need reliable data. Data engineers process the source data from tools and applications before it reaches the end consumer. 

But what if the data does not show the expected values? These are some of the questions related to bad data that we often hear:

1. Why does this data size look off?
2. Why are there so many nulls?
3. Why are there 0’s where it should be 100’s?

Bad data can waste time and resources, reduce customer trust, and affect revenue. Your business suffers from the consequences of data downtime—a period when your data is missing, stale, erroneous, or otherwise compromised. 

It is not acceptable that the data teams are the last to know about data problems. To prevent this, companies need complete visibility of the data lifecycle across every platform. The principles of software observability have been applied to the data teams to resolve and prevent data downtime. This new approach is called data observability.

What is Data Observability?

Data observability is the process of understanding and managing data health at any stage in your pipeline. This process allows you to identify bad data early before it affects any business decision—the earlier the detection, the faster the resolution. With data observability, it is even possible to reduce the occurrence of data downtime.

Data observability has proved to be a reliable way of improving data quality. It creates healthier pipelines, more productive teams, and happier customers.

DataOps teams can detect situations they wouldn’t think to look for and prevent issues before they seriously affect the business. It also allows data teams to provide context and relevant information for analysis and resolution during data downtime. 

Pillars of Data Observability

Data observability tools evaluate specific data-related issues to ensure better data quality. Collectively, these issues are termed the five pillars of data observability. 

• Freshness
• Distribution
• Volume
• Schema
• Lineage

These individual components provide valuable insights into the data quality and reliability. 

Freshness

Freshness answers the following questions:

• Is my data up-to-date?
• What is its recency?
• Are there gaps in time when the data has not been updated?

With automated monitoring of data intake, you can detect immediately when specific data is not updated in your table. 

Distribution

Distribution allows us to understand the field-level health of data, i.e., is your data within the accepted range? If the accepted and actual data values for any particular field don’t match, there may be a problem with the data pipeline.

Volume

Volume is one of the most critical measurements as it can confirm healthy data intake in the pipeline. It refers to the amount of data assets in a file or database. If the data intake is not meeting the expected threshold, there might be an anomaly at the data source. 

Schema

Schema can be described as data organization in the database management system. Schema changes are often the culprits of data downtime incidents. These can be caused by any unauthorized changes in the data structure. Thus, it is crucial to monitor who makes changes to the fields or tables and when to have a sound data observability framework.

Lineage

During a data downtime, the first question is, “where did the data break”? With a detailed lineage record, you can tell exactly where. 

Data lineage can be referred to as the history of a data set. You can track every data path step, including data sources, transformations, and downstream destinations. Fix the bad data by identifying the teams generating and accessing the data.

Benefits of using a Data Observability Solution

Prevent Data Downtime

Data observability allows organizations to understand, fix and prevent problems in complex data scenarios. It helps you identify situations you aren’t aware of or wouldn’t think about before they have a huge effect on your company. Data observability can track relationships to specific issues and provide context and relevant information for root cause analysis and resolution.

Increased trust in data

Data observability offers a solution for poor data quality, thus enhancing your trust in data. It gives an organization a complete view of its data ecosystem, allowing it to identify and resolve any issues that could disrupt its data pipeline. Data observability also helps the timely delivery of quality data for business workloads.

Better data-driven business decisions

Data scientists rely on data to train and deploy machine learning models for the product recommendation engine. If one of the data sources is out of sync or incorrect, it could harm the different aspects of the business. Data observability helps monitor and track situations quickly and efficiently, enabling organizations to become more confident when making decisions based on data.

Data observability vs. data monitoring

Data observability and data monitoring are often interchangeable; however, they differ.

Data monitoring alerts teams when the actual data set differs from the expected value. It works with predefined metrics and parameters to identify incorrect data. However, it fails to answer certain questions, such as what data was affected, what changes resulted in the data downtime, or which downstream could be impacted. 

This is where data observability comes in. 

DataOps teams become more efficient with data observability tools in their arsenal to handle such scenarios. 

Data observability vs. data quality

Six dimensions of measuring data quality include accuracy, completeness, consistency, timeliness, uniqueness, and validity. 

Data quality deals with the accuracy and reliability of data, while data observability handles the efficiency of the system that delivers the data. Data observability enables DataOps to identify and fix the underlying causes of data issues rather than just addressing individual data errors. 

Data observability can improve the data quality in the long run by identifying and fixing patterns inside the pipelines that lead to data downtime. With more reliable data pipelines, cleaner data comes in, and fewer errors get introduced into the pipelines. The result is higher quality data and less downtime because of data issues.

Signs you need a data observability platform

Source: What is Observability by Barr Moses

• Your data platform has recently migrated to the cloud
• Your data team stacks are scaling with more data sources
• Your data team is growing
• Your team is spending at least 30% of its time resolving data quality issues. 
• Your team has more data consumers than you did 1 year ago
• Your company is moving to a self-service analytics model
• Data is a key part of the customer value proposition

How to choose the right data observability platform for your business?

The key metrics to look for in a data observability platform include:

1. Seamless integration with existing data stack and does not require modifying data pipelines.
2. Monitors data at rest without having to extract data. It allows you to ensure security and compliance requirements.
3. It uses machine learning to automatically learn your data and the environment without configuring any rules.
4. It does not require prior mapping to monitor data and can deliver a detailed view of key resources, dependencies, and invariants with little effort.
5. Prevents data downtime by providing insightful information about breaking patterns to change and fix faulty pipelines.

Conclusion

Every company is now a data company. They handle huge volumes of data every day. But without the right tools, you will waste money and resources on managing the data. It is time to find and invest in a solution that can streamline and automate end-to-end data management for analytics, compliance, and security needs. 

Data observability enables teams to be agile and iterate on their products. Without a data observability solution, DataOps teams cannot rely on its infrastructure or tools because they cannot track errors quickly enough. So, data observability is the way to achieve data governance and data standardization and deliver rapid insights in real time. 

Episode 8 – How to Build a Cloud-Scale Monitoring System

In Episode 8 of the Infralytics Show, Shankar interviewed Molly Struve. Molly is the Lead Site Reliability Engineer for DEV Community, an online portal designed as a place where programmers can exchange ideas to help each other. The discussion focused on two topics, “How to build a cloud-scale monitoring system” and “How to scale your Elastic Stack for cloud-scale monitoring.” 

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How Molly started working in software engineering and cloud-scale monitoring

Molly earned an aerospace degree from MIT after originally thinking she would study software engineering. She said that since all engineering degrees provide students with the same core problem-solving skills, so when she later decided to work in the software engineering field, she already had the problem-solving background she needed in order to make the transition. The reason she didn’t end up going the aerospace route is that you have to be located in California or Washington where the aerospace industry is but she is from Chicago and didn’t really want to move. It’s good to know that people with various different educational backgrounds have still been able to find success in software engineering!

Let’s jump into the discussion of cloud-scale monitoring! Here are the key points Molly made in reference to the topics listed above.

The Interview – building a cloud-scale monitoring system

What are some of the key requirements to look for when you build out a large cloud-scale monitoring system?

When you start monitoring, you just want coverage, and to do that you often start adding all of these different tools and before you know it you have 6, 7, or 8 different tools doing all this monitoring. However, when the time comes to use it you have to open up all these different windows in your browser just to piece together what is actually going on in your system. So, one of the key things she tells people when they are building a monitoring system is that they have to consolidate all of the reporting. You can have different tools, but you need to consolidate the reporting to a single place. Make sure everything’s in one place so it’s a one stop shop to go and find all the information you need.

When an alert triggers, it must require an action so alert fatigue is a big problem in many monitoring systems. When you have a small team it might seem fine to have exceptions that everyone knows when you don’t respond to certain alerts, but as your team gets larger you have to tell new engineers what the exceptions are, and this process just simply doesn’t scale. So you have to be very disciplined in responding to alerts.

The goal is to get to a point where whoever is on call, whether it’s one person, two people, or three people, can handle the error workload that is coming into the system by way of alerts. 

In the beginning, when you are setting up a monitoring system you might have a lot of errors, and you just have to fix stuff and the improvement of the system comes with time. The ideal metric is zero errors, so you need to be aware of when errors get to a point where they need to be addressed.

Monitoring from an infrastructure perspective is different from monitoring from a security perspective

Trying to figure out what to monitor is also very challenging. You have to set up your monitoring and adjust it as you go depending on what perspective you are monitoring for. Knowing what to monitor is a little bit based on trial and error. That way, if there is data that you wish you had monitoring for, you can address the error and then go in and add the necessary code so that it’s there in the future. After you do that a few times you will end up with a really robust system so the next time an error occurs, all the information you need will be there and it might only take you a few minutes to figure out what’s wrong.

Beyond bringing the data together and optimizing alerting, what are the other best practices?

Another best practice is tracking monitoring history. When trying to solve the error from an alert, you will want to know what the past behavior was. Past behavior can help you debug a problem. What were you alerted about in the past and how was the problem addressed then?

Also, you have to remove all manual monitoring for your monitoring system to be truly scalable. Some systems require employees to check a dashboard every few hours, but this task is easily forgotten. So, if you want a monitoring system to scale you have to remove all manual monitoring. You don’t want to rely on someone opening up a file or checking a dashboard to find a problem. The problem should automatically come to you or whoever is tasked with addressing it. 

What tools did you use to automate?

At Kenna we used datadog. It’s super simple, it integrates really easily with ruby which is the language I primarily work with.

Anything else important on the topic of best practices for cloud-scale monitoring?

Having the ability to mute alerts when you are in the process of fixing them is important. When a developer is trying to fix a problem, it’s distracting to have an alert going off repeatedly every half hour. Having the ability to mute an alert for a set amount of time like an hour or a day can be very helpful. 

What else is part of your monitoring stack?

The list goes on and on. You can use honeybadger for application errors, AWS metrics for your low-end infrastructure metrics, StatusCake for your APIs to make sure your actual site is up, Elasticsearch for monitoring, circleci for continuous integration. It’s a large list of many different tools, but we consolidated them all through datadog. 

What kind of metrics did your management team look for?

Having a great monitoring system allows you to catch incidents and problems before they become massive problems. It’s best to be able to fix issues before the point at which you would have to alert users to the problem. You want to solve problems before they impact your user base. That way on the front-end it looks to the user like your product is 100% reliable, but it’s just because developers have a system on the backend that alerts them to problems so they can stop them before they directly impact users. Upper management obviously wants the app to run well because that’s what they are selling and the monitoring system allows for that to happen.

How big was the elasticsearch cluster where you worked before?

The logging cluster that we used at Kenna had 10 data nodes. The cluster we used for searching client data was even bigger. It was a 21 node cluster. 

What were some of the problems when it came to managing this large cluster?

You want to be defining what you are logging. and make it systematic. Early on at Kenna we would be logging user information we would end up with a ton of different keys which created more work for elasticsearch. This also makes searching and using the data nearly impossible. To avoid this you need to come up with a logging system by defining keys and making sure that everyone is using those keys when they are in the system and logging data. 

We set up our indexes by date, which is common. When you get a month out from the date on a specific index, you want to shrink them to a single shard, which will decrease the number of resources that elasticsearch needs in order to use that index. Even further out than that, you eventually should close that index so that elasticsearch doesn’t need to use any resources for it. 

Any other best practices for cloud-scale monitoring?

Keep your mapping strict and that can help you to avoid problems. If you are doing the searching yourself, try to use filters rather than queries. Filters run a lot faster and are easier on elasticsearch so you want to use them when you are searching through data.

Finally, educating your users on how to use elasticsearch is important. If developers don’t know how to use it correctly, elasticsearch will time out. So, teach users how to search keys, analyzed fields, unanalyzed fields, etc. Also, this will help your users get the targeted, accurate data they are looking for so educating them on how to use elasticsearch is for their benefit as well. Internal users at Kenna (which is who is being referred to here) were conducting searches through Kibana. Clients would interface with the data relevant to them (after training) through an interface that the Kenna team built which prevented clients from doing things that could take down the entire cluster. 

So are you using elasticsearch in your current role at DEV?

DEV is currently using a paid search tool, but we hope to switch to elasticsearch because elasticsearch is open source and it will give us more control over our data and how we search it.

There’s an affordable solution for achieving the best practices described

Molly described the importance of consolidating reporting, responding to alerts, avoiding alert fatigue, automating alerts and reports, and tracking monitoring history. Just two weeks prior to this interview, Shankar gave a presentation about avoiding alert fatigue, and this relevant topic keeps becoming a focus of discussions. Many of the points Molly made, from the importance of automating alerts and reports to the importance of consolidating reporting, are the reasons we started Skedler. 

Are you looking for an affordable way to send periodic reports from elasticsearch to users when they need it? Try Skedler Reports for free! 

Do you want to automate the monitoring of elasticsearch data and notify users of anomalies in the data even when they aren’t in front of their dashboards? Sign up for a free trial of Skedler Alerts!

We hope you are enjoying our podcast so far. Happy holidays to all of our listeners. We will be taking a short break, but will be back with new episodes of The Infralytics Show in 2020!

Episode 7 – Best Practices for Implementing Observability in Microservices Environments

In this episode of Infralytics, Shankar interviewed Stefan Thies, the DevOps Evangelist at Sematext, a provider of infrastructure and application performance monitoring and log management solutions including consulting services for Elastic Stack and Solr. Stefan also has extensive experience as a product manager and pre-sales engineer in the Telecom domain. Here are some of the key discussion points from our interview with Stefan on implementing observability in microservices!

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Microservices based on containers have become widely popular as the platform for deployingsolutions in public, private or hybrid clouds. What are the top monitoring and management challenges faced by organizations deploying container based microservices that want to implement observability?

There are quite a lot of challenges. Some people start simply with a simple host and later use orchestration tools like Kubernetes and what we see is that containers add another infrastructure layer and a new kind of resource management. At the same time we are monitoring performance with a new kind of metrics. What we developed in the past was special monitoring agents to collect these new kinds of metrics on all layers, so we have a cluster node with performance metrics for the specific node, on top of Kubernetes ports and in the port you want several containers and multiple processes, and first, new monitoring agents need to be container aware so they have to collect metrics from all of the layers. 

The second challenge is the new way of dynamic deployment and orchestration. You deal with more objects than just servers and your services, because you also deal with cluster nodes, containers, deployment status of your containers. This can be very dynamic and orchestrators like Kubernetes move your applications around so maybe an application fails on one node and then the cluster shifts the application to another node. It’s very hard to track errors and failures in your application. So the new orchestration tools add additional challenges for DevOps people, because they need to see not only what happens on the applications but at the cluster level. Additional challenges are also added because things are moving around. There is now another layer of complexity added to the process. 

What are additional challenges that come with containers? What should administrators be looking for?

There are metrics on every layer; servers, clusters, ports, containers, deployment status. Also another challenge is that lock management has also changed completely. You need a logging agent that’s able to collect the container logs. With every logline we add information on which node it is on, in which port it is deployed, and which container and container image, so we have better visibility. The next thing that comes with container deployment is microservices. Typically architectures today are more distributed and split into little services that work closely together, but it’s harder to trace transactions that go through multiple services. Transaction tracing is a new pillar of the observability but it requires more work to implement the necessary code. 

Basically, log management becomes a challenge because of all of these microservices and you are also doing the tracing not just on the metrics and events, but you are also now looking at all of the trace files. So having more data requires people to have larger data stores.

How do you consolidate the different datasets?

We use monitoring agents and logagents. Both tools use the same tags so the logs and metrics can be correlated.

How do you standardize the different standards and practices?

With open source, it’s a lot of do-it-yourself which means you need to sit down and think what metrics do I have, what labels do I need, and do the same for the logging and for the monitoring.

What are your recommended strategies for organizations?

More and more users are used to having 24/7 services because they are used to getting that from Google and Facebook. All the big vendors offer 24/7 services. Smaller software vendors really have a challenge to be on the same level to be aware of any problem as soon as possible. 

What you need to do is to first start monitoring; availability monitoring, then add metrics to it for infrastructure monitoring. Are your servers healthy? Are all the processes running? Then the next level is education monitoring to check the performance of your databases, your message queues, and the other tools you use in your stack, and finally the performance of your own applications. 

When it comes to troubleshooting and you recognize some service is not performing well, then you need the logs. In the initial stage typically people use SSH, log into the server, try to find the log file, and look for errors. You need to collect the logs from all your servers, from all of your processes, and from all of your containers. Index the data and make it searchable and accessible. If you want to be really advanced you go to the level of code implementation and tracing. 

What is observability? How is it different from monitoring?

Observability is the whole process. Monitoring, you have metrics, you have logs, and transaction tracing to have code level visibility. This process allows you to pinpoint where exactly the failure happens so it’s easier to fix it. When you have more information, it’s much faster to solve the problem. 

How would an organization move from just monitoring to observability?

At Sematext our log management is very well accepted so people typically start with collecting the logs because it’s the first challenge they have; Where do I store all these logs? Should I set up a seperate server for it, or do I go for Software as a Service? These are the types of questions people are asking, so we see that people start collecting logs and then they start to discover more features and that we offer monitoring and then they start installing monitoring agents and they start to ask about specific applications. Automatically they start to do more and more steps. That is the process that our customers normally follow.

Are you interested in learning more about what Stefan’s company offers? Go to www.sematext.com. Are you looking for an easy-to-use system for data monitoring that provides you with automated delivery of metrics and provides code-free and easy to manage Alerts? Check out Skedler!

If you want to learn more tips from experts like Stefan, you can read more articles about the Infralytics video podcast on our blog!

Episode 6 – Cybersecurity Alerts: 6 Steps To Manage Them

Is your Security Ops team overwhelmed by cybersecurity alerts? In this episode of The Infralytics Show, Shankar, Founder, and CEO of Skedler, describes the seemingly endless number of cybersecurity alerts that security ops teams encounter. 

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The Problem Of Too Many Cybersecurity Alerts

Just to give you an understanding of how far-reaching this problem is, here are some facts. According to information published in a recent study by Bitdefender, 72% of CISOs reported alert or agent fatigue. So, don’t worry, you aren’t alone. A report published by Critical Start found that 70% of SOC Analysts who responded to the study said they investigate 10+ cybersecurity alerts every day. This is a dramatic increase from just last year when only 45% said they investigate more than 10 alerts each day. 78% spend more than 10 minutes investigating each of the cybersecurity alerts, and 45% reported that they get a rate of 50% or more false positives. 

When asked the question, If your SOC has too many alerts for the analysts to process, what do you do? 38% said they turn off high-volume alerts and the same percentage said that they hire more analysts. However, the problem that arises with the need to hire more analysts, is that more than three quarters of respondents reported an analyst turnover rate of more than 10% with at least half reporting a 10-25% rate. This turnover rate is directly impacted by the overwhelming number of cybersecurity alerts, but it raises the question, what do you do if you need to hire more analysts to handle the endless number of alerts, but the cybersecurity alerts themselves are contributing to a high SOC analyst turnover rate. It seems a situation has been created where there are never enough SOC analysts to meet the demand. 

To make matters worse, more than 50% of respondents reported that they have experienced a security breach in the past year! Thankfully, you can eliminate alert fatigue and manage alerts effectively with these 6 simple steps.

The Solution To Being Overwhelmed By Cybersecurity Alerts

1. Prioritize Detection and Alerting

According to Shankar’s research, step 1 is that business and security goals and the available resources that you have at your disposal to use to achieve them must prioritize threat detection and alerting. Defining what your goals are is a great way to start. Use knowledge of your available resources to better plan how you are going to respond to alerts and how many you will be able to manage per day. 

2. Map Required Data

Step 2 is to map your goals and what you are trying to achieve to the data that you are already capturing. Then you can see if you are collecting all of the required data to adequately monitor and meet your security requirements. Identify the gaps in your data by completing a gap analysis to see what information you are not collecting that needs to be collected, and then set up your telemetry architecture to collect the data that is needed.

3. Define Metrics Based Cybersecurity Alerts

Step 3 is to define metrics based alerts. What type of alerts are you going to monitor? Look for metric-based alerts that often search for variations in events. Metric based alerts are more efficient than other types of alerts, so Shankar recommends this to those of you who are at this step. You should augment your alerts with machine learning.

Definitely avoid cookie cutter detection. The cookie cutter approach is more of a one size fits all organizations approach that most definitely will not be the best approach for YOUR organization. Each organization has its own unique setup, and you need to have your own setup that is derived from your own security goals.  Also, optimize event-based detection but keep these to a minimum so that your analysts do not end up getting overwhelmed by the alerts.

4. Automate Enrichment and Post-Alert Standard Analysis

Once you have set up these rules, the next step is to see how you can automate some of the additional data that your analysts need for their analysis. Can you automate the enrichment of the alert data so that your analysts don’t have to go and manually look for additional data to provide more context to the alerts? Also, 70-80% of the analysis that an analyst goes through as part of the investigation of an alert is very standard. So ask yourself, is it possible to automate it?

5. Setup a Universal Work Bench

• Use a setup similar to what Kanban or Trello uses where you have a queue and the alerts that need to be investigated are moved from one stage to the next. This will help you keep everything organized. This can help you arrange the alerts in order of importance so that your analysts know which alerts to address first.

• Add enriched data to these alerts, so automate the enrichment process to make sure it is readily available for your analysts through the work bench.

• Provide more intelligence to the alerts (adding data or whatever else is needed to provide context). This will help you provide a narrative for the alerts and this will help you use immersion learning to come up with recommendations that your security analysts can investigate.

These first five steps are not intended to be a one time initiative but rather a repetitive process where each step can be perfected over a long period. 

6. Measure and Refine

• Continuous improvement – measure the effectiveness of your alert system. How many alerts are flowing into the system, how much time is it taking for your analysts to investigate each of the alerts, and what is the false-positive rate vs. the true-positive rate.

• Iterative approach- Think of a sprint-based approach? What changes can you make to improve your results in the next sprint iteration? Add more data or change your alert algorithms for different results and be more precise.

By making regular changes to improve your results, you can reduce the operations costs of your organization and provide more security coverage, reducing the overall likelihood of a major cybersecurity breach.

If you are looking for alerting and reporting for ELK SIEM or Grafana that is easy to use check out Skedler. Interested in other episodes of the Infralytics Show? Check out our blog for the Infralytics Show videos and articles in addition to other informative articles that may be relevant to your business!

Episode 5 – Elasticsearch Data Leaks: Top 5 Prevention Steps

For this week’s episode, Shankar discussed Elasticsearch data leaks with Simone Scarduzio, Project Lead at ReadOnlyREST, a security plugin for Elasticsearch and Kibana. Before we jump into the interview on how you can prevent an Elasticsearch data leak, here is some context on why this topic is especially relevant today.

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Recent Elasticsearch Data Leaks

There were three instances of massive data leaks involving Elasticsearch databases just in the week prior to our interview with Simone. 

1. An Elasticsearch database containing the records of 2.5 million customers of Yves Rocher, a cosmetics company, was found unsecured. 
2. A database containing the personal data of the entire population of Ecuador (16.6 million people) was found unsecured. 
3. An Elasticsearch database containing personally identifiable information linked to 198 million car buyer records was found unsecured.

The frequent occurrence of Elasticsearch database data leaks raises the question, “How can we prevent a data leak in Elasticsearch data stores?” For the answer, we interviewed an Elasticsearch security expert and asked his opinion on the top 5 data leak prevention techniques.

What are the Root Causes of These Data Leaks?

The common theme among these different data leaks regarding what caused them was related to the outsourcing contracts. Contracts should not only include the functional requirement but should also include a security requirement. The good thing is that solutions already exist and they are free. 

If you think about Amazon Elasticsearch Service, it’s very cheap and convenient. However, you can’t install any plugin in Amazon because it’s blocked. So a developer will just find a way around this problem without a viable security plugin, which ultimately leaves the database vulnerable. So a lot of the issue has to do with how Amazon built the Amazon Elasticsearch Service. They split the responsibility for security between the user and the infrastructure manager, which is them (Amazon), so Amazon is not contractually liable for the problems that arise regarding security.

Amazon allows anyone to open up an Elasticsearch cluster without any warning. Simone says he “does not agree with this practice. Amazon should either avoid it or have a very big warning” so that data leaks like the three recent ones can be avoided.

Another problem is that the companies that had these clusters exposed had a massive amount of data accumulated, and Simone says that “even if it was secure, it is not a good practice and the entities that created the GDPR would not agree with the practice” of holding that much data in such a way. It is almost like they were inviting a data breach.

5 Ways To Prevent An Elasticsearch Data Leak

If you have an Elasticsearch cluster and want to keep it protected follow these rules:

1. Remember that data accumulation is a liability and you should only collect what is necessary at all times. Every piece of data should have an expiration date. 
2. Every company from the minute they obtain user data should accept the responsibility it comes with and should center their attention on the importance of data handling and data management. Outsource access to the data less, but keep all of the different objectives of the different actors in line at all times.
3. Use security plugins. When you accumulate data, the security layer should be as close as possible to the data itself.
4. Use encryption on the http interface and between the Elasticsearch nodes for next-level security.
5. Rigorously implement local data regulations and laws like the GDPR in the European Union. 

If you are looking to increase the security for your elasticsearch cluster, using a security plugin is a great security measure to start with and can help you prevent a data leak from exposing your clients’ data. Learn more about ReadOnlyREST’s security plugin for Elasticsearch and Kibana here.

The Infralytics Show

Thanks for reading our article and tuning in to episode 5 of the Infralytics show. We have a great show planned for next week as well, so be sure to come back! Interested in checking out your past episodes? Here’s a link to episode 4.

Kibana Reporting in Action: A Kane LPI Case Study

Every company carries valuable data, whether it’s relevant to a specific client’s private information, statistics, or finances. Alongside the fear that you might lose precious data or incur a security breach with faulty programs, data needs to be filed and exported accurately in order to meet certain deadlines and practical standards; which is why Kibana reporting has proven to be an imperative tool for loss prevention within any given company.

The Company

Kane LPI Solutions is a prime example of how Kibana reporting achieved higher marketability within the cognoscente program Skedler. A trusted provider of Third Party Administration services for more than 15 years, Kane LPI has issued over US$11 billion of offshore annuity and investment products for an extensive global client base. They needed a robust solution to help prevent financial and reputation losses as well as boost KPIs. As it stood, competition was fierce — there were many much larger players out there on the field.

The Challenge

The company’s practical processes specifically involved sending sensitive and accurate post-trading files on a timely basis. Kane LPI’s clients had strict operational requirements for control and compliance: if the time window for sending files was missed, financial loss was borne both for them and the company. Delays like this could incur strict penalties by settlement and clearing corporations such as The Depository Trust and Clearing Corporation (DTCC). With so much at stake, our challenge was to fundamentally satisfy operational concerns to prevent those losses, as well as improve pragmatic business flow.

Kibana Reporting: A Means of Automation

Kibana reporting allowed Kane LPI to send out daily scheduled reports to both clients and internal users from thousands of lines of log entries within multiple systems, serving as a key monitoring tool and satisfying auditing requirements in the process. As a result, Skedler became a critical go-to ELK stack tool for this company and its clients, allowing Kane LPI to send out reports to their clients and internal users, which the company couldn’t do before.

Developers were able to receive automated error reports at the beginning of the day, allowing them to ensure information is sent to regulatory and settlement organizations to meet deadlines. Simultaneously, managers received daily and weekly reports on SLA performance and non-compliance, enabling them to take remedial steps to prevent recurrence and to protect time-sensitive transactions. Vendors also received daily and weekly reports on SLA performance and non-compliance of their product in KPI solution, which fundamentally reused the investment of energy and time in an ELK stack based solution.

As a precautionary measure, Skedler introduced a critical line of defence to errors by inaugurating manager and software vendors with automated reporting sent at the beginning of the day, which described potential errors during transfer of files, allowing future errors to be minimized. We then added another layer of security by administering any issues during batch runs from the previous 24 hours. 

Statistically, all of these enhancements enabled Kane LPI’s clients to avoid up to $5 million per month in trade and exchange losses, as well as protect their well-nurtured reputation as a high-quality provider.

Ready to start saving time by creating, scheduling and distributing Kibana reports automatically? Try Skedler for free.

The Top 3 ELK Stack Tools Every Business Intelligence Analyst Needs in 2017

A version of this post, updated for 2018, can be found here: The Top 5 ELK Stack+ Tools Every Business Intelligence Analyst Needs.

The world’s most popular log management platform, ELK Stack, has ultimately reflected its nifty, modernized capabilities with this recent statistic: each month, it is downloaded 500,000 times. So what makes ELK Stack and ELK Stack Tools just so attractive? In many cases, it fulfills what’s really been needed in the log analytics space within SaaS: IT companies are favoring open source products more and more. Since it’s based on the Lucene search engine, Elasticsearch is a NoSQL database which forms as a log pipeline tool; accepting inputs from various sources, executing transformations, then exporting data to designated targets. It also carries enhanced customizability, which is a key preference nowadays, since program tweaking is more lucrative and stimulating for many engineers. This is coupled with ELK’s increased interoperability, which is now a practically indispensable feature, since most businesses don’t want to be limited by proprietary data formats.

ELK Stack tools which simply higher-tier those impressive elements will elevate data analysis just that little bit further; depending on what you want to do with it, of course.

Logstash

Elite tool Logstash is well-known for its intake, processing and output capabilities. It’s mainly intended for organizing and searching for log files, but works effectively for cleaning and streaming big data from all sorts of sources into a comprehensive database, including metrics, web applications, data stores, and various AWS services. Logstash also carries impressive input plugins such as cloudwatch and graphite, allowing you to sculpt your intelligence to be as easy to work with as possible. And, as data travels from source to store, those filters identify named fields to accelerate your analysis; deciphering geo coordinates from IP addresses, and anonymizing PII data. It even derives structure from seemingly unstructured data.

Kibana 5

Analysis program Kibana 5.0 boasts a wealth of new refurbishments for pioneering intelligence surveying. Apart from amplified functionalities such as increased rendering, less CPU usage, and elevated data and index handling, Kibana 5.0 has enriched visualisations with interactive platforms, leveraging the aggregation capabilities of Elasticsearch. Space and time auditing are a crucial part of Kibana’s make up: the map service empowers you to foresee geospatial data with custom location data on a schematic of your selection, whilst the time series allows you to perform advanced generation analysis by describing queries and transformations.

Skedler

ELK Stack reporting tool, Skedler, combines all the automated processes you’d never dream you could have within one unit. Fundamentally, it ups your speed-to-market auditing with cutting-edge scheduling, which Kibana alone does not offer; serving as a single system for both interactive analysis and reporting. Skedler methodically picks up your existing dashboards in the server for cataloging, whilst also enabling you to create filters, refine specific recipients, and filter file folders to use whilst scheduling. Additionally, Skedler automatically applies prerequisite filters with generate reports, preserving them as defined; and encompasses high-resolution PDF and PNG options to incorporate in reporting, which sequentially eliminates the need for redundant reporting systems.

There you have it, the top ELK stack tools no business intelligence analyst should ever be without!

Ready to start streamlining your analysis and start reporting with more stability? Right now, we’re offering a free trial.

Are You Wasting Time Manually Sending Kibana Reports?

Automated processes are, invariably, becoming more and more integral to our everyday lives, both in and out of the office. They’ve replaced much of the manual workforce and have improved systematic procedures, which otherwise would be at the mercy of various human error elements as well as higher risks of data breaches. This, as well as recognizing manual reporting as time-consuming labour, are some key issues we don’t need to worry about any more by virtue of processing automation; Kibana being one of those favorable products.

Focus on What Matters

As a result of businesses adopting bots as part of our everyday processes, we’re left with the far more creative aspects of information science (which automation hasn’t quite caught up with yet). Naturally, Elasticsearch’s aesthetically enhanced data delivery is one of its chief selling points: users are able to explore unchartered data with clear-cut digital graphics at their very disposal. This significant upgrade in data technology has allowed us to possess more varied and complex insights; it’s more exciting now than it has ever been before.

In contrast, however, tedious tasks such as email deliveries of reports to customers, compliance managers and other stakeholders remain arduous and time-consuming; deterring attention from more stimulating in-depth data analysis. What we know to be necessary is for analysts to have the time available to devote themselves to exploring Tableau’s analytics, instead of undergoing mundane processes such as manual spreadsheet creation, generating, email exporting, and distributing.

Automate Kibana Reports

Perhaps it’s possible that you’ve already started utilizing Kibana without realizing the perks of automated scheduling. Luckily, Skedler can completely undertake those prosaic tasks, at an affordable price. As an automated scheme which meets full compliance and operations requirements, Skedler allows your peers, customers and other stakeholders to be kept informed in a virtually effortless and secure way. Comprehensive exporting preferences such as PDF, XLS and PNG are also serviceable; allowing you the luxury of consigning instant or scheduled report generation in the format you desire.

Additionally, Skedler’s reporting motions are facilitated through its prestigious dashboard system, which automatically discovers your existing Kibana dashboards and saved searches to make them available for reporting – again, saving you time creating, scheduling and sending Kibana reports. All your filtered reporting and data chartering is available on a single, versatile platform; meaning you won’t spend extensive amounts of time searching through your outgoing email reports for a specific item.

Skedler simply allows you to examine all of your criteria through one umbrella server with clear functionalities to separate the stunning data visualization deliveries, and the slightly less exciting archive of manual spreadsheet generation and handling for other departments, which it can totally manage by itself.

Ready to start saving time by creating, scheduling and distributing Kibana reports automatically? Try Skedler for free.

3 Apps to Get the Most Out of Kibana 5.0

A new financial quarter starts, full-scale data appraisals are once again at the forefront for every business’ sales agenda. Luckily, Elasticsearch’s open source tool Kibana 5.0 is the talk of the town – and for good reason.

Improvements since version 4.0 are unequivocally noticeable. Its new and far more sleek user interface display not only wows in terms of visuals (note the subsidiary menu that minimizes when not in use), but demonstrates impressive UI capabilities that allows you to reach data far more effectively. The new CSV upload, for example, has the potential to catch a much wider data spread, transforming it to index mapping that’s effortlessly navigable. Its new management tab allows you to view the history of the files with associated data, as well as Elasticsearch indexes where you actively send log files.

This version’s huge boost in code architecture grants the potential for more augmentations than ever, especially with split code self-contained plugins with open-end code tweaking, resulting in several lucrative alpha and beta versions. And it’s essentially allowed us the privilege to now ask: what kind of data insight does my company really need, and which app is best to harness it?

1. Logz.io

Logz.io has fundamentally enriched Kibana with two major touches: increased data security, and more serviceable enterprise sequences as a result. Take their access user tokens, for example, which enable share visualizations and dashboard with those who aren’t necessarily Logz.io users, rather than the URL share function. You can pretty much be as selective with your data as you so please; specific and cross-referenced filter searches are an added function to the tokens. This makes it easy to attach pre-saved filters when back in Kibana.

2. Skedler

Skedler has specifically focused developed reporting capabilities with actionables to perform on data, effectively meaning you can do more with it all in a proactive way. Scheduling is an integral part of this program’s faculty, as it works with your existing database searches and dashboards; allowing you to organize dispatches daily, weekly, monthly and so on. Again, you’re able to action specific filters as and when you’re scheduling, making your reports as customized as needed when sending for peer review.

3. Predix

Predix has established itself as a strong contender for effective data trend sweeps, such as HTTP responses, latencies and visitors – and you’re able to debug apps at the sam e time. Combining this with Kibana’s exhaustive data visualizations and pragmatic dashboard, controlling and managing your log data not only highly secure, but it allows you to become more prognostic when forecasting future data.

Ready to save hours generating, scheduling and distributing PDF and XLS reports from your
Elasticsearch Kibana (ELK) application to your team, customers and other stakeholders? Try Skedler for free.