Our data engineers are trained by Databricks and have the necessary skills to deliver rapid success with careful planning of your data journey.
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 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.
The Databricks Runtime is a data processing engine built on a highly
version of Apache Spark, for up to 5x performance gains. It runs on
infrastructure for easy self-service without DevOps, while also
and administrative controls needed for production. We have experience
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.