Data Analytics Infrastructure Using the Data Lake Concept
Even just a few years ago, data lakes were once the “Wild West” of data analytics infrastructures. No one seemed sure how to curate, manage, or secure access. Storing data from multiple sources in a common location was sensible to minimize any resistance. However, it was difficult to keep the business data organized.
Today, data lake teams are much more productive and more structured, with active data lake management. Databricks sets the standard for collaborative development practices.
It also provides the highest-performance data lake technologies for data science and data engineering. Data engineering has been the clear winner from data lake advances with huge innovation in harmonizing corporate data management practices.
The Databricks Lakehouse Platform
You can’t talk about data lakes without mentioning the Databricks platform – the clear leader in the world of data lakes. Databricks sets the standard for collaborative development practices. It also provides the highest-performance technologies for data science and data engineering.
Data lakes still struggle to deliver on exposing their data innovation to the business. Your typical business analyst or report user can’t sift through thousands of datasets. They also can’t deal with the intricacies of accessing files or coding in Python or Scala. Spark SQL has been around for a while but was largely inaccessible to most.
Databricks introduced the Delta file format, which provides some update and delete capabilities and a SQL interface to the data lake, making the environment look more like a database. These are useful for data engineering activities or ad hoc exploration. But they may not meet the expectations of a business intelligence (BI) user who desires consistent performance and interactive analytics.
The state of the art in data lake file formats is also split between Delta Lake, Iceberg, and Hudi, adding to future interoperability concerns.
Best Practices for Data Lake Implementation
Take a look at any real-world data lake or data lakehouse implementation and the solution is clear. You will always find a data warehouse solution sitting alongside the data lake serving business teams. The familiarity and connectivity of a SQL relational data warehouse and the ready connectivity to the full ecosystem of BI and Analytics tools, both modern and legacy, provide overwhelming advantages.
Compared with file-based technologies and code-heavy approaches, database technologies are several orders of magnitude better at delivering high-performance answers to business questions at scale.
Certain classes of data engineering tasks will even run cheaper and faster against the data warehouse. Data science and data integration tools are increasingly able to push down tasks to the data warehouse without developer intervention. Yellowbrick’s data warehouse architecture delivers the scale and performance needed and fixed operating costs that budget owners truly appreciate.
Match Your Analytics Aspirations with a Data Warehouse
Rather than trying to eradicate the data warehouse as part of a data lake program, most organizations are better off modernizing legacy relational approaches to familiar but modern cloud-native relational data warehouse technologies such as Yellowbrick.
Minimizing rewrites of business analytics and reports and eliminating re-skilling teams leads to massive cost savings and improvements in productivity. Adding Yellowbrick’s focus on extreme platform efficiency you get a massive win for data teams and budget decision makers – bringing together the best of data lake technologies and relational data warehouse worlds.
Other vendors are pivoting to this strategy with Snowflake introducing SnowPark and Azure Synapse Analytics aligning Synapse Spark and Synapse Dedicated SQL Pools. These vendors of course want to lock you into their ecosystem.
If you are running a successful data lake program on Databricks, consider partnering with Yellowbrick Data Warehouse to fully realize the aspiration of your data strategy without vendor lock-in.
See why Yellowbrick and Databricks are perfect partners HERE.
