Data Ingestion Pipeline
A data ingestion pipeline moves streaming data and batched data from pre-existing databases and data warehouses to a data lake. Businesses with big data configure their data ingestion pipelines to structure their data, enabling querying using SQL-like language. Organization of the data ingestion pipeline is a key strategy when transitioning to a data lake solution.
For an HDFS-based data lake, tools such as Kafka, Hive, or Spark are used for data ingestion. Kafka is a popular data ingestion tool that supports streaming data. Hive and Spark, on the other hand, move data from HDFS data lakes to relational databases from which data could be fetched for end users.
Two considerations when selecting a data ingestion tool:
- The data storage format to be used when storing the data on disks is dependent on how your organization plans on consuming data and can be in column- or row-based storage formats.
- What your optimal data compression technique for both storage and retrieval should be.
Data ingestion is complicated. SnapLogic’s elastic big data integration platform as a service (iPaaS) coupled with SnapLogic eXtreme can help you achieve your data ingestion strategy without your needing to write code, saving you time and money. SnapLogic pipelines allow for simple drag-and-drop features for designing with the right connectors for different data storage formats and compression techniques.
Learn more about data ingestion pipelines.