Modern analytics, data science, AI, machine learning … your analysts, data scientists and business innovators are ready to change the world. If you can’t deliver the data they need, faster and with confidence, they’ll find a way around you. (They probably already have.)
Data lakes hold vast amounts of a wide variety of data types and make processing big data before uploading to destinations like Snowflake and applying machine learning and AI possible. How can you ensure that your data lake integration delivers data continuously and reliably?
Basic design pattern for cloud data lake integration
Your cloud data lake is the gateway to advanced analytics. Once ingested, data can go in many different directions to support modern analytics, data science, AI, machine learning, and other use cases. A basic data ingestion design pattern starts by reading data from a data source, then routes the data with simple transformations such as masking to protect PII, and stores data in the data lake.
One of the challenges to implementing this basic design pattern is the unexpected, unannounced, and unending changes to data structures, semantics, and infrastructure that can disrupt dataflow or corrupt data. That’s data drift, and it’s the reason why the discipline of sourcing, ingesting and transforming data has begun to evolve into data engineering, a modern approach to data integration.
Smart Data Pipelines for Cloud Data Lake Integration
What smart data pipelines do
Managing infrastructure change
The StreamSets approach to data integration and data engineering makes it possible to change infrastructure endpoints without starting over. For example, if the source of your data lake ingestion pipeline changes from an Oracle database to MySQL, you have 3 options: