Avocado Datalake Architecture

A high level design architecture of our proposed solution for your organization to manage all sources of data into a unified Data Lake.

RDBMS to centralized Data Lake Architecture
Full support of reading structured, semi-structured and unstructured data sources into centralized Data Lake with cloud storage such as AWS S3, GCP GCS, and Azure Blob Storage.

For structured data sources, we support relational databases such as MySQL, PostgreSQL, SQL Server, Amazon Aurora DB, and Cloud SQL. For semi-structured data sources, we support systems like MongoDB, Amazon DynamoDB, and Google Cloud Bigtable. For unstructured data sources, includs Apache Kafka streams, as well as formats such as CSV, JOSN, or Parquet. Avocado Data Lake pipelines can efficiently ingest all of them into a centralized data lake. By leveraging open table storage formats like Apache Hudi, Delta lake or Apache Iceberg, we ensure Change Data Capture (CDC) and enable optimized read and write operations.

To maximize the value of your Data Lake, we implement advanced metadata management using AWS Glue Data Catalog, Unity Catalog, or GCP Data Catalog. This enables seamless data discovery and analysis through tools like Amazon Athena, Presto, Apache Airflow, Looker and Looker Studio. Our expertise extends to data governance and security, providing best practices for table access and permissions using AWS Lake Formation.
Further more we can connect your Data Lake storage data into enterprise data warehouse such as Amazon Redshift and BigQuery.
We partner with organizations to unlock the full potential of their data and drive data-driven decision making.
For more details about our Avocado Datalake, visit our Products Pages and to learn more about our implementation and bootstrapping services, check out our Blogs Pages