Updated for 2018.
The world’s most popular log management platform, ELK (Elasticsearch, Logstash and Kibana) Stack, is a powerful tool for business intelligence. But most businesses rely on multiple sources of data and need a way to analyze it all to improve the system and allocate resources properly.
The Elastic stack has many integrations with different log management tools. However, it isn’t the ideal tool in every business case, especially when you have multiple data sources. Given the breadth of tools in the marketplace, how do you decide which ones to add to your stack for optimal BI analysis? We put together our list for the top business intelligence tools to compliment ELK stack in 2018.
What makes ELK Stack tools just so attractive? 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.
Microsoft Power BI is the most cost-efficient cloud-based option for analyzing and visualizing business intelligence data. Power BI offers ease-of-use, excel-based add ons and the ability to use browser- and desktop-based authoring with apps and platforms – both on-premise and in the cloud. Additionally, Microsoft Power BI is the only one of these tools to provide extensive R and big data integrations.
While Power BI doesn’t integrate with ELK stack natively, you can automate export of ELK data or use the REST API to combine it with both on-premise and cloud based datasets for complex data analysis. Moreover, it has added data preparation, data discovery and data dashboards recently for enterprise users. The Power BI Suite is delivered on the Microsoft Azure Cloud platform, with the Power BI Desktop provided on-premise as a stand-alone option.
Tableau is known as the industry leader in business intelligence data visualization. While it has a fairly steep learning curve to get all the value (like both Power BI and Qlik), it is the leader in ease of use. A non-technical user will still be able to create dashboards and get insights using the drag-and-drop interface. For advanced embedded analytics, Tableau is the clear leader. However, its architecture falls behind Qlik in supporting self-contained ETL and data storage.
Tableau can connect to most data sources through their more stable and mature APIs, and has added support for R. Like Power BI, Tableau doesn’t offer native integration to ELK, but you can automate export and import of ELK data into Tableau with connectors. They have been making several improvements to the UI, including improvements to the data wrangling and responsive mobile app. Additionally, Tableau has focused on expanding support for more complex data federation workflows and other feature requirements of large enterprise companies.
Qlik allows data exploration beyond the pattern-recognition capabilities of SQL data structures and queries with a powerful in-memory engine, though Qlik is not as good at advanced embedded analytics as Tableau. Qlik is well-known for developing enterprise-level products and great customer service that makes scaling more efficient. With a streamlined onboarding process, Qlik makes generating insights and value quick as well.
Similar to other visual analytics tools, Qlik doesn’t offer direct integration to ELK platform and requires export/import. Qlik’s functionality is always being improved, and it already has one of the leading Application Programming Interface (API) command sets in analytics. QlikView is a powerful reporting engine that goes beyond visual dashboards, and it goes beyond Tableau in its wide variety of integration points.
For data scientists, Anaconda is a great addition for prescriptive analytics and machine learning. It is an open source, freemium package manager (with conda), environment manager, a Python and R distribution and collection of open source packages. One of the biggest advantages of Anaconda is it just works right away. You can import ELK data into anaconda and build custom machine learning models that meet your business requirements.
No, Slack is not a BI tool, but the team messaging and collaboration platform is one of the best ways to keep everyone up to speed. Apart from the productivity features, such as group channels and direct messaging, Slack has a huge number of integrations. There’s one for almost every enterprise product available. Plus, it’s much more fun to use than email. Slack is an excellent tool to distribute and consume reports and alerts from your ELK platform.
ELK Stack reporting and alerting tool Skedler combines all the automated processes you’d never dream you could have within one affordable unit. Fundamentally, it simplifies scheduling and distribution of relevant data as reports and alerts from ELK platform. With faster your speed-to-market, you can focus on more important things.
Reports can be print-ready, high-resolution PDFs or analysis-ready CSV/XLS reports generated periodically from ELK platform. Alerts deliver timely information about anomalies in ELK data. Skedler delivers both reports and alerts via Slack in addition to email. You can also set up Alerts to trigger webhooks. By automating the export of data, Skedler can serve as the simple, time-saving bridge between your ELK platform and Analytics toolkit.
There you have it, the top ELK Stack+ tools no business intelligence analyst should ever be without!
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What are your favorite tools? What is critical to your stack?
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