Data Lake/Other sources to Data Warehouse (AWS Redshift, GCP BigQuery, Snowflake)

A data lake stores raw, unstructured data from various sources, while a data warehouse stores structured and processed data, often for specific business intelligence (BI) and analytical purposes, Utilise the Avocado Datalake highly scalable and configurable ETL pipeline to ingest the data into enterprise data warehouse like AWS Redshift, GCP BigQuery, Azure Synapse Analytics or even in Snowflake.
Data lake or other sources data to Data warehouse using Avocado Datalake ETL pipelines
A highly configurable and scalable ETL pipelines for syncing data into enterprise Data Warehouse using Avocado Datalake pipelines, contact to setup in your organization [email protected]
Streamline Your Data Warehouse Ingestion with Our Scalable Solutions

As a leading data consultancy, we specialize in building highly scalable and configurable data ingestion pipelines to power your data warehouse. Our expertise in technologies like Scala and Apache Spark/Flank enables us to create robust ETL solutions for seamlessly integrating data from diverse sources, including data lakes, relational databases (e.g., Aurora DB, Cloud SQL), and more.

Our "Do-It-Yourself" framework offers a modular design where Extraction, Transformation, and Loading processes are independent, providing you with the flexibility to customize organization-specific logic through simple plug-ins. We support integration with popular data warehouse platforms such as AWS Redshift, GCP BigQuery, and Azure Synapse Analytics.

Partner with us to simplify your data warehousing, improve efficiency, and unlock valuable insights from your data. We have already built the extraction from data lake and ingestion into data wareshouse using the Factory Design Pattern and remaining work is to set it up with different parameters as per oraganization's requirement. The sample pipeline code is shown below and configuration parameters can be passed separately:

1val pipeline = sparkStage
2  .andThen(extract)
3  .andThen(transform)
4  .andThen(load)
5
6pipeline(())
So, we just need to select an appropriate format for extracting data from sources—such as Apache Hudi, Apache Iceberg, or Delta Lake tables—followed by transformations tailored to your organization's requirements, and then load or ingest the data into your preferred data warehouse, such as BigQuery, Redshift, or Azure Synapse Analytics. The only task is to pass the configuration/ set up the configuration into our codebase and it will handle the ingestion as a DIY.
Contact us today to discuss your specific needs and how our tailored solutions can drive your business forward, email us at
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