Metadata-driven ETL/ELT pipelines into Azure Data Lake and Databricks—batch, streaming, and hybrid workloads.
Learn moreMetadata-driven process of collecting and importing raw data from various sources into a data lake for further processing and analysis. Expertise in building data lakes after transferring data from source databases.
Design and implement scalable data lake architectures that can handle diverse data types and volumes. Our solutions provide flexible storage and processing capabilities for structured, semi-structured, and unstructured data.
Build robust Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines that efficiently move data from source systems to your data lake with proper validation and error handling.
Metadata management for data lineage, cataloging, and governance through the ingestion lifecycle.
Set up real-time data streaming capabilities for continuous data ingestion from IoT devices, applications, and other live data sources using technologies like Kafka, Kinesis, and Event Hubs.
Implement security measures and governance frameworks to protect sensitive data during ingestion, including encryption, access controls, and compliance with regulatory requirements.
Optimize data ingestion performance through parallel processing, efficient partitioning strategies, and intelligent resource allocation to handle large-scale data volumes efficiently.
Our ingestion approach covers source identification through validation, loading, and monitoring.
Identify and catalog all data sources including databases, APIs, files, and streaming sources
Extract data using optimized connectors and APIs while maintaining source system performance
Apply data transformations, cleansing, and validation rules to ensure data quality
Load processed data into the data lake with appropriate partitioning and indexing strategies
Azure Data Factory, AWS Glue, Google Dataflow
Apache Kafka, Azure Event Hubs, AWS Kinesis
Apache Spark, Apache Flink, Databricks
Delta Lake, Apache Parquet, Azure Data Lake
Apache Airflow, Azure Data Factory, AWS Step Functions
Apache Atlas, Azure Purview, DataDog
We help teams design ingestion architecture, migrate legacy ETL, and stand up pipelines on Azure and Databricks.
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