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8 Best Practices for Effective Data Lake Ingestion

Big Data 5 MIN READ April 18, 2023
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Frequently Asked Questions

How to ingest data into Data Lake

To ingest data into a Data Lake, you can use various methods such as batch processing, streaming, or event-driven processing. Batch processing involves loading data in large batches from various sources, while streaming involves continuously processing and ingesting data in real-time. Event-driven processing is used for ingesting data when a specific event occurs, such as a user clicking on a website or an application generating an error. To perform data ingestion, you can use tools like Apache Spark, Apache Kafka, AWS Kinesis, or Azure Event Hubs, among others.

What is the difference between data ingestion and data integration?

Data ingestion is the process of collecting and importing data from various sources into a storage or computing system, while data integration refers to the process of combining data from different sources and integrating it into a unified view.

How can data compression reduce costs in a Data Lake architecture?

Data compression can reduce costs by minimizing storage requirements and lowering CPU costs. However, it’s important to choose the right compression format to avoid increasing costs.