Which tools could you use for bringing all data out of a data lake?
💡 Model Answer
To extract all data from a data lake and load it into a target system, you can use a variety of ETL or ELT tools. In the cloud, AWS Glue is a serverless ETL service that can crawl S3, infer schemas, and run Spark jobs to transform data before loading into Redshift or Snowflake. Azure Data Factory offers pipelines that can read from ADLS Gen2 and write to Synapse or Cosmos DB. Google Cloud Dataflow (Apache Beam) can stream or batch data from Cloud Storage into BigQuery. Open‑source options include Apache NiFi for data flow orchestration, Talend Open Studio for ETL, and dbt for transformation in the warehouse. For large‑scale batch jobs, you can use Spark on EMR or Databricks to read Parquet/ORC files from the lake, transform, and write to a data warehouse. All these tools provide connectors, scheduling, and monitoring to ensure data is reliably moved out of the lake.
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