Lets get to know Databricks

The only open unified platform for data management, business analytics and machine learning.

Users achieve faster time-to-value with Databricks by creating analytic workflows that go from interactive exploration and ETL through to production data products.

AI applications are simpler to explore and transition to production as the one platform is used by data scientists and data engineers. Users can quickly prepare clean data at massive scale and continuously train and deploy state-of-the-art ML models for best-in-class AI applications.

Databricks makes it easier for its users to focus on their data by providing a fully managed, scalable and secure cloud infrastructure that reduces operational complexity and total cost of ownership.

Why HiFX !

HiFX is a Certified Consulting & System Integrator Partner of Databricks which allows us to leverage their cloud-based platform’s Unified Analytics expertise, solution architects and sales resources to better help our customers.


We achieve 100X performance gains, 30% rapid deployment & 20% more stabilization effectivity

  • Working with dataBricks since 2017
  • Databricks trained consultants & developers

“We have deployed mission critical and highly scalable data pipeline & analytics solution using Databricks”


Mohan Thomas

Co-Founder and Director, Technology & Projects

Data lakehouses

Now combine the capabilities of Data Lakes and Data Warehouse to enable BI and ML on all data.

Data lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes.

Merging them together into a single system means that data teams can move faster as they are able to use data without needing to access multiple systems. Data lakehouses also ensure that teams have the most complete and up to date data available for data science, machine learning and business analytics projects.

A Lakehouse has the following key features:

  • • Transaction support
  • • Schema enforcement and governance
  • • Support for BI
  • • Storage is decoupled from compute
  • • Openness
  • • Support for diverse data types ranging from unstructured to structured data
  • • Support for diverse workloads
  • • End-to-end streaming

Delta Lake

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. Accelerate the velocity that high quality data can get into your data lake and the rate that teams can leverage that data, with a secure and scalable cloud service.



Data is stored in the open Apache Parquet format, allowing data to be read by any compatible reader. APIs are open and compatible with Apache Spark™.


Data lakes often have data quality issues, due to a lack of control over ingested data. Delta Lake adds a storage layer to data lakes to manage data quality, ensuring data lakes contain only high quality data for consumers.


Handle changing records and evolving schemas as business requirements change. And go beyond Lambda architecture with truly unified streaming and batch using the same engine, APIs and code.

Who uses Databricks ?

Data Engineers
  • Develop, test, execute and monitor batch ETL jobs
  • Implement data streaming ingestion or analytics job
  • Collaborate on code,notebook and jobs
Data Analysts
  • Monitor machine learning process
  • Develop production machine learning pipelines
  • Explore machine learning models
Data Scientists
  • Perform data analysis using SQL at scale
  • Explore datasets visually and interactively in a notebook environment


The Real Time Streaming and analyzing of Big Data can help companies to uncover hidden patterns, correlations and other insights. Companies can get answers from it almost immediately being able to upsell and cross-sell clients based on what the information presents.

The existence of Real Time Streaming data technology brings the type of predictability that cuts costs, solves problems and grows sales. It has led to the invention of new business models, product innovations and revenue streams.

  • Unified and simplified architecture across batch and streaming to serve all use cases
  • Robust data pipelines that ensure data reliability with ACID transaction and data quality guarantees
  • Reduced compute times and costs with a scalable cloud runtime powered by highly optimized Spark clusters
  • Elastic cloud resources auto- scale up with workloads and scale down for cost savings
  • Modern data engineering best practices for improved productivity, system stability and data reliability

Databricks Workspace

Databricks Runtime

Databricks Cloud Service