Setting up high quality data with great performance

Databricks Consulting

We have been working with Databricks since 2017 and have picked up niche skills in the advanced capabilities of its various platform components.

Our data engineers are trained by Databricks and have the necessary skills to deliver rapid success with careful planning of your data journey.

Data Journey Process Consulting

A data journey begins with the plan for setting up high quality data with great performance. Our consultants can help with the designing, implementing and managing a scalable platform for ingestion, data pipeline, data lake and consumption of the data.

Data Ingestion involves pulling data from all the data sources and storages, different types of data including batch and streaming data for real time analytics.

Data Pipelines involves processing the data using distributed Spark runtimes using Scala and Python.

Data Lake is where the processed data is stored for analysis considering security and storage performance.

Delta Lake/Delta Engine Consulting Services

Delta Lake brings reliability, performance, and lifecycle management to data lakes. No more malformed data ingestion, difficulty deleting data for compliance, or issues modifying data for change data capture. With Delta Lake, you can accelerate the velocity at which high quality data can get into your data lake and offer the following features. Accelerate all your workloads on your data lake with Delta Engine, a new query engine designed for speed and flexibility. It’s built from the ground up to deliver fast performance on modern cloud hardware for all data use cases across data engineering, data science, machine learning, and data analytics.

Features

  • ACID Transactions: Multiple data pipelines can read and write data concurrently to a data lake. ACID Transactions ensure data integrity with serialisability, the strongest level of isolation.
  • Updates and Deletes: Delta Lake provides DML APIs to merge, update and delete datasets. This allows you to easily comply with GDPR/CCPA and simplify change data capture.
  • Schema Enforcement: Specify your data lake schema and enforce it, ensuring that the data types are correct and required columns are present, and preventing bad data from causing data corruption.
  • Time Travel (Data Versioning): Data snapshots enable developers to access and revert to earlier versions of data to audit data changes, rollback bad updates or reproduce experiments.
  • Scalable Metadata Handling: Delta Lake treats metadata just like data, leveraging Spark’s distributed processing power. This allows for petabyte-scale tables with billions of partitions and files.
  • Open Format: All data in Delta Lake is stored in Apache Parquet format enabling Delta Lake to leverage the efficient compression and encoding schemes that are native to Parquet.
  • Unified Batch and Streaming Source and Sink: A table in Delta Lake is both a batch table, as well as a streaming source and sink. Streaming data ingest, batch historic backfill, and interactive queries all just work out of the box.
  • Schema Evolution: Big data is continuously changing. Delta Lake enables you to make changes to a table schema that can be applied automatically, without the need for cumbersome DDL.
  • Audit History: The Delta Lake transaction log records details about every change made to data, providing a full history of changes, for compliance, audit, and reproduction.
  • 100% Compatible with Apache Spark API: Developers can use Delta Lake with their existing data pipelines with minimal change as it is fully compatible with Spark, the commonly used big data processing engine.

Databricks Runtime Setup

The Databricks Runtime is a data processing engine built on a highly optimised version of Apache Spark, for up to 5x performance gains. It runs on auto-scaling infrastructure for easy self-service without DevOps, while also providing security and administrative controls needed for production. We have experience setting up this environment for development and production workloads.

Our experience working with the Databricks runtime has shown that there is a significant increase in the performance compared to the open source version of Spark, thus improving productivity with better management of costs and offering administrative control.

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