Our products

1. RDBMS Data to Data Lake

Ingest structured data from RDBMS sources into centralized data lake in your cloud storage e.g: AWS S3, GCS etc for AI/ML and data analytics.

  • MySQL
  • AWS Aurora
  • Amazon RDS
  • Cloud SQL
  • PostgreSQL
RDBMS to centralized Data Lake Architecture
Full support of reading RDBMS (through JDBC connection or parsing binlog) and ingestion into centralized Data Lake in cloud storage such as // AWS S3, GCP GCS, and Azure Blob Storage.

Our RDBMS data lake solutions enable you to seamlessly ingest data from relational databases like MySQL, AWS Aurora, and PostgreSQL into your data lake. This allows you to leverage your existing structured data readly avialable for advanced analytics and machine learning initiatives or with heavy workload without connecting to production RDBMS databases.

2. Semi-Structured Data to Data Lake

Process and analyze semi-structured data in your data lake for enhanced insights.

  • MongoDB
  • AWS DynamoDB
  • Google BigTable
  • Any other NoSQL
  • Kafka messages
Semi-Structured databases data into centralized Data Lake Architecture
Readly available codebase for reading data from NoSQL database and writing to centralized datalake.

Our semi-structured data pipeline ETL job will allow you to process and transform data from sources like MongoDB, and AWS DynamoDB into your centralized data lake. This enables you to gain deeper insights from your data and improve your decision-making using AI/ML, Data Insights or Data Analytics teams.

3. Unstructured Data to Data Lake

Leverage unstructured data into your centralized data lake for advanced analytics and machine learning using our highly configurable the ETL pipeline.

  • CSV Files
  • JSON Files
  • Web APIs
  • Text Documents
Unstructured to centralized Data Lake Architecture
configurable or parttern based reading of unstructured files and writing into centralized datalake with required cleaning and transformation.

Our unstructured data lake solutions enable you to leverage data from sources like CSV files, JSON files, web APIs, and text documents for advanced analytics and machine learning. This allows you to unlock the full potential of your data and gain a competitive advantage once your data available into centralized data lake.

Once the data available in the data lake table formats such as:
  • Apache Iceberg
  • Apache Hudi
  • Delta Lake
  • Apache XTable
with interoperability using the Apache Xtable, we attached with centralized data catalog for discovery and utilization using the below tools as per your organization needs.
  • AWS Glue Data Catalog
  • GCP Data Catalog
  • Apache Atlas:
  • Databricks Unity Catalog
After the data is available through any of the above centralized catalog as per your organization needs, we will help you build the access controls using the IAM or Lakeformation in the AWS Glue data catalog. Access controls can be provisioned through our specialized Terraform module, as per your need to provide access to various groups in your organization, or you can configure the access yourself. Exmaples of intended access controls are:
  • AI/ML Engineers
  • LLM Engineers
  • Data Engineers
  • Data Scientists
  • Data Analysts
  • Business Intelligence
Our High level architecture for the above centralized data lake and end user or utilization from various tools or groups of engineers is as below:

Data driven aproach through various tools

For more information on Avocado Datalake codebase or want to get full access and support for any of the above propose soltution then do let's setup a free call with me or email us at