Avocado Datalake

Avocado Datalake simplifies data lake management for your organization.

We are a data lake & data warehouse consultancy specializing in transforming raw data into actionable insights. Our end-to-end solutions encompass data ingestion, storage, management, and discovery. We seamlessly integrate data from diverse sources — structured, semi-structured, and unstructured — into a centralized Data Lake, built on cloud storage like AWS S3, Google Cloud Storage, or Azure Blob Storage.

Avocado Datalake Architecture

A high-level architecture of our proposed solution for managing all sources of data into a unified Data Lake.

RDBMS, GCS files, and NoSQL sources to a centralized Iceberg Data Lake Architecture — As a Data Lake and Data Warehouse Consulting company, we provide full support for ingesting structured, semi-structured, and unstructured data sources into a centralized Data Lake on AWS S3, GCP Cloud Storage, or Azure Blob Storage.

For structured data sources, we support relational databases such as MySQL, PostgreSQL, SQL Server, Amazon Aurora, and GCP Cloud SQL. For semi-structured sources, we support MongoDB, Amazon DynamoDB, and Google Cloud Bigtable. For unstructured and file-based sources, we handle Apache Kafka streams as well as CSV, JSON, Parquet, Avro, and ORC files stored on GCS, S3, or Azure Blob.

By leveraging open table formats — Apache Hudi, Delta Lake, and Apache Iceberg — we ensure Change Data Capture (CDC), ACID-like guarantees, schema evolution, and optimized read/write operations across all cloud platforms.

To maximize the value of your Data Lake, we implement advanced metadata management using AWS Glue Data Catalog, Unity Catalog, or GCP Dataplex. This enables seamless data discovery through Amazon Athena, Presto, Trino, and Looker Studio.

Once data is curated in your lake, we can connect it to enterprise warehouses such as Amazon Redshift, BigQuery, or Azure Synapse Analytics.

Our Data Engineering Products

Production-ready, highly configurable ETL/ELT pipelines delivered as codebase — set up in your own cloud account in days, not months.

RDBMS to Data Lake

Batch, incremental, and CDC pipelines from MySQL, PostgreSQL, SQL Server, Oracle, AWS Aurora, and GCP Cloud SQL into Iceberg, Hudi, or Delta Lake.

MySQLPostgreSQLCDCDebezium

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NEWGCP Cloud SQL → Iceberg Lakehouse

HOCON-driven Spark JDBC-to-Iceberg ELT pipeline on GCP Dataproc Serverless — incremental checkpoints, upserts, and Airflow/Livy orchestration. Up to 98% cheaper than Cloud Datastream.

GCPDataproc ServerlessApache IcebergAirflow

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NEWGCS Files → Iceberg Lakehouse

Checkpoint-driven Spark pipeline that ingests Parquet, CSV, JSON, and Avro files from Google Cloud Storage directly into Apache Iceberg tables — with idempotency guards and schema inference.

GCSParquetApache IcebergCheckpoints

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Semi-Structured Sources to Data Lake

Ingest MongoDB, DynamoDB, Firestore, Cassandra, and JSON/XML feeds into your data lake on AWS, GCP, or Azure with automated Spark pipelines and full data cataloguing.

MongoDBDynamoDBNoSQLJSON

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Unstructured Data to Data Lake

File-based ingestion pipelines for CSV, JSON, Parquet, Avro, ORC, and web API responses — with Spark ETL, data cataloguing, and open table format support.

CSVParquetWeb APIsApache Spark

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Data Lake → Data Warehouse

Sync curated data lake tables into BigQuery, Redshift, Snowflake, or Azure Synapse Analytics — DIY YAML/HOCON configuration, no bespoke code required.

BigQueryRedshiftSynapseOLAP

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Featured Technical Blogs

Deep-dive guides, cost benchmarks, and hands-on tutorials covering Data Lake architecture, Apache Iceberg, Apache Hudi, Delta Lake, and GCP data engineering.

Avocado Datalake