For the past 30 years, the primary data source for business intelligence (BI) and data visualization tools has generally been either a data warehouse or a data mart.
But as enterprises today struggle to cope with the growing complexity, scale, and speed of data, it’s becoming clear that the data tools of 30 years ago weren’t designed to handle the enterprise data management challenges of today - especially with the growing variety and amounts of data that enterprises are generating.
That’s why a growing number of organizations are adopting modern data lake platforms as the primary storage repository for enterprise data.
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Data lakes make it simple and cost-effective for enterprises to aggregate and retain data in its raw source format, and data engineers can integrate BI and data visualization tools with a data lake to enable downstream analytics. However, downstream analysts and BI users often find that data lakes don’t provide the same level of performance and accessibility that they could depend on from data warehouse solutions.
In this blog, we’ll explore why enterprises are increasingly keen to adopt data lake solutions and share two simple ways to integrate BI and data visualization tools with a data lake. You’ll also learn about the challenges enterprises face when using data lakes to support BI, and how to overcome those challenges with modern analytics technologies.
Why Integrate BI and Data Visualization Tools with a Data Lake?
Virtually all data-driven companies have one or more medium-high maturity data warehouse initiatives, where data is ingested into a data warehouse using some form of the ETL process, stored in a structured format, and made available to business analysts in subject-oriented databases known as data marts.
In the data warehousing paradigm, making new data available for analytics means defining a schema for the data, building an ETL pipeline to ingest it into the data warehouse, and maintaining that pipeline as long as it’s needed - all without necessarily knowing how the data will eventually be used.
The challenge for enterprise IT is that managing a data warehouse and making data available for business analysts becomes increasingly time-consuming, complex, and costly as the quantity of data and the number of data sources increases. Each new ETL pipeline consumes additional compute resources and must be built and maintained by data engineers whose time is highly valued.
For enterprises experiencing exponential big data growth, defining schema and building ETL pipelines to make all of that data available for analysts becomes prohibitively resource-intensive. Often, the only way to mitigate growing ETL costs is to discard data that doesn’t have a clear use case, even though it might hold valuable insights.
To retain more of their data and avoid the high resource cost of scaling the ETL process, enterprises are increasingly adopting modern data lake solutions, which function as a centralized repository where enterprise data can be stored in its raw format - structured, semi-structured, or unstructured.
If enterprise data engineers can integrate BI and data visualization tools with a data lake, downstream analysts and BI users can start analyzing data in the lake to uncover insights that can inform business decision-making - or at least, that’s what everyone would like to have happen.
In fact, BI users are encountering significant challenges when using traditional BI and data visualization tools on the data lake.
Integrating BI and Data Visualization Tools with a Data Lake: 3 Key Challenges
1. Non-Relational Data Structures
99% of today’s popular data visualization and business intelligence tools were originally designed to analyze data in a structured or relational format.
Data warehouses store data in relational tables that are compatible with traditional BI and data visualization tools, but data lakes often store data in non-relational formats (e.g., JSON, Parquet, Avro, ORC, etc.) that can’t easily be analyzed with the BI and data visualization tools that business analysts have available.
If BI users want to analyze unstructured data in the data lake, they need data engineers to help by transforming the data into a relational format. Sometimes this means defining schema, building an ETL pipeline, and making the data available through a data warehouse or data mart. Other companies are using data preparation software tools directly in the data lake to apply schema and make data available for analytics.
In either case, the end result is that analysts become heavily dependent on IT and data engineers to support their analytics needs by preparing data and making it accessible in a format that can be analyzed with BI and data visualization tools.
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2. Swampy Data Lakes
Data lakes make it easy and cost-effective for enterprises to aggregate and store data from many sources in a single centralized location. But in the absence of appropriate data quality and governance measures, a data lake quickly turns into a data swamp: a disorganized mass of data that’s poorly maintained, difficult to query, and provides little or no value.
As a starting point for extracting business insights from the data lake, analysts need to know precisely what data is available to be queried. This is often achieved by implementing a data catalog that serves as an inventory of raw and defined data sets and helps analysts find the data they need within the data lake.
Organizations should implement data governance and quality controls that help to ensure the accuracy, completeness, consistency, reliability, and recency of data that enters the data lake.
3. Poor Query Performance
A third challenge when it comes to using traditional BI and data visualization tools on the data lake is slow query performance. While the relational tables in a data warehouse are in the optimal format for querying, data lakes often take much longer to query - especially as they grow in size.
Poor query performance can interrupt the analytical workflow for BI users, prevent thorough data exploration, and stifle the creative thinking that produces good analysis and valuable insights.
A new category of software products, known as Analytics Query Accelerators (AQAs), is now emerging to help address the challenge of poor query performance when enterprises integrate BI and data visualization tools with a data lake. AQAs plug directly into the data lake, providing capabilities that make data more accessible and performant for BI and data visualization use cases.
Now let’s look at three strategies for integrating data visualization and BI solutions with a data lake and how each one addresses these three key challenges.
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How to Integrate BI and Data Visualization Tools with a Data Lake
Data lakes allow organizations to cost-effectively capture, aggregate, and store large amounts of data. With so much data coming in, there's a huge opportunity for downstream analysts and BI users to query the data, analyze it, and extract actionable insights.
But before analysts can start working with the data, enterprise DevOps and IT must find a way to integrate BI and data visualization tools with the data lake in an architecture that’s both cost-effective and performant. Check out these three general strategies for integrating BI and data visualization tools with a data lake:
Passing Data Through Your Data Warehouse
Data lakes often contain data stored in unstructured formats such as JSON. Business users would like to analyze this data, but most BI and data visualization tools were built to analyze relational tables and lack the capability to analyze data in unstructured formats.
Data engineers can make unstructured data available for analysis by transforming it into a tabular format which can be analyzed with BI tools. In some cases, data preparation tools can work directly on the data lake to transform the data into a structured format. In other cases, data engineers will build and maintain ETL pipelines on a per-request basis, making specific data available to downstream analysts.
As you might have guessed, passing data through your data warehouse to enable analytics is considered highly inefficient. Any notion of data democratization is laid to rest as analysts and BI users become fully dependent on IT and data engineers to enable data access by fulfilling time-consuming ETL requests. Data engineers can often become inundated with requests for data access and may end up spending hours every week building and managing ETL pipelines.
Choosing a BI Platform with Data Lake Connectors
As an alternative to passing data through the data warehouse, organizations can adopt an end-to-end BI platform that connects directly to a data lake. Some BI and data visualization platforms feature integrated tools that help clean, transform, and prepare unstructured data for business intelligence analytics.
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Tableau and Microsoft Power BI
These two leading BI and data visualization tools can connect to Azure Data Lake Storage (ADLS) Gen2 to analyze structured or unstructured data stored there. Tableau users can use the included Prep Builder tool to prepare and transform data directly in Tableau prior to analysis, while Microsoft Power BI users can do self-service transformation using dataflows or with the Query Editors tool.
Amazon QuickSight
Amazon QuickSight is AWS' native BI tool and allows users to connect with software-as-a-service (SaaS) applications such as Salesforce or ServiceNow, third-party databases such as MySQL, Postgres, and SQL Server, as well as native AWS services including Amazon Athena, an interactive query service that allows them to analyze unstructured data in Amazon S3 data lakes using standard SQL queries. While QuickSight doesn’t connect directly to the data lake, integration with Amazon Athena allows BI users to query data inside the lake without having to move data or build an ETL pipeline.
Do any BI tools integrate natively with Data Lakes?
Options are fairly limited when it comes to choosing a BI platform that integrates natively with data lake storage. Most BI tools are built to connect with data warehouses - not data lakes - and there are very few available data lake products that integrate natively with BI platforms. For organizations that do choose this route, data swamps and poor query performance can still result in poor value realization.
Adopting a Data Lake Platform with BI Capabilities
A third strategy for organizations is to adopt a cloud data platform with built-in BI and data visualization capabilities.
A cloud data platform gives customers the ability to store data at scale in a data lake, normalize or transform the data using data preparation tools, and analyze the data with built-in or connected data science, BI, and data visualization tools.
With this type of solution, the work of integrating BI and data visualization tools with the data lake has already been done by the developers, leaving customers ideally positioned to reap the benefits of data lake analytics without the complexity and management overhead of configuring integrations or building ETL pipelines.
ChaosSearch is the perfect example of a modern data lake platform with integrated BI and data visualization capabilities. Our platform transforms your Amazon S3 into a hot data lake for analytics in the cloud, delivering embedded analytics via integration with Kibana, an open source data visualization tool that lets you query, visualize, and build dashboards using the data in your lake - all with no data movement and no ETL process.
ChaosSearch also includes proprietary technologies that help address the challenges of using BI and data visualization on the data lake. We index your data with Chaos Index®, our proprietary representation that compresses your data by up to 95%, renders it fully searchable and improves query performance on the data lake. Our platform also includes Chaos Refinery®, a collection of tools for virtually cleaning, preparing, and transforming indexed data and preparing virtual views prior to analysis.
Use ChaosSearch to Connect BI and Data Visualization with Your Data Lake
ChaosSearch delivers a data lake platform with integrated BI and data visualization capabilities, helping our customers overcome the key challenges of integrating BI and data visualization tools with a data lake.
Additional Resources
Read the Blog: How to Index and Process JSON Data for Hassle-free Business Insights
Listen to the Podcast: The Human Element of Tech Development
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