Databricks

More and more companies these days are expressing the need for tools to address use-cases that arise in the data environment. This is one of the main reasons why Databricks created a multicloud lakehouse platform that spans use-cases of data engineering, ML, AI, data analytics and data visualization. Databricks offers a unified and collaborative platform designed for data processing and analysis, machine learning, and implementation of big data-based solutions.

As a Databricks partner, we provide consultancy in the design and development of solutions built on the lakehouse platform, fully leveraging its flexibility, scalability, and power.

Contatti di riferimento

Federico Sala

Data Architect

Andrea Gioia

Chief Technology Officer

Overview

Databricks is a cloud platform designed to fully harness the potential of data and can be used on the major cloud providers (Azure, AWS, GCP). It provides an integrated environment for data processing and analysis, machine learning model training, and dashboard development. These are the key features that distinguish the platform:

Unified:

a single platform for data integration, storage, analysis, AI model development, and training, capable of working with both structured and unstructured data. It enables the utilization of major programming languages in the market (Python, SQL, Scala, R) within a collaborative IDE based on notebooks.

Open:

It leverages the most widely used open-source tools and projects in the data domain:

Apache Spark for batch and streaming processing in a distributed computing pattern.
Delta Lake as a storage format enabling ACID transactionality for data stored within the data lake.
MLflow for managing the lifecycle of machine learning models, including experimentation, serving, and tracking.

Scalable:

takes full advantage of the underlying cloud technology to cost-effectively achieve high performance by scaling the infrastructure according to the required load

The Databricks platform

The Databricks suite consists of several modules that address the many needs that arise during the engineering of an enterprise data platform.

Data Engineering

The design and data integration are at the core of every data-centric platform. Databricks combines the power of distributed processing with Apache Spark and the storage flexibility of Delta Lake to provide a fully managed and highly simplified ETL/ELT development experience. Databricks Notebooks enable the development of ETL logic flows using Python, SQL, or Scala, while Delta Live Tables allow defining dependencies between developed notebooks and creating workflows. Additionally, Databricks offers automated ingestion tools: Auto Loader facilitates ingestion from cloud storage into the data lake, ensuring the idempotence of imported data.

Data Streaming

More and more applications are facing the issue of real-time data streaming. Databricks uses Apache Spark Structured Streaming to work with streaming data and to manage incremental uploads within the data lake.

Data Science & Machine Learning

The Machine Learning module enriches the functionality of the platform with a suite of tools dedicated to Data Scientists and ML Engineers. It provides an integrated environment that simplifies ML and MLOps development processes by allowing the entire lifecycle of machine learning models to be managed. In fact, Databricks ML allows:

The training of models, both manual and automatic
The tracking and sharing of features used in training processes via a fully managed feature store
The tracking of model parameters and performance via MLflow
Model serving via registry and integrated Databricks services

Analytics & BI

Very often, data engineering needs are coupled with warehousing and analytics requirements. The Databricks platform combines the computing power and reliability of storage to perform analytical queries. It offers a dedicated UI for data analysts where they can launch queries on data in the lakehouse and build visualisations via dashboards.

Data Governance

The quality, integrity, compliance and security of data assets are elements that should not be underestimated in a data-centric platform. This is why Databricks offers a unified governance service for the lakehouse that enables the implementation of the practices, policies and procedures required by the company. Through the Unity Catalog, platform administrators can manage permissions for teams and individuals at a low level via Access Control Lists (ACLs). In addition, the Unity Catalog allows the segregation of responsibilities and data, so that each user can read and view only the portions of data to which they actually have access (row and column-level security).

DevOps and CI/CD

Databricks offers services that simplify development and deployment processes in both the ETL and ML fields. We are talking about common tools for versioning, automating, scheduling and releasing code, as well as tools for monitoring executions, all packed into a single platform. Databricks offers Databricks Repos, which allow integration with the most common git providers, and Databricks Workflows, which allow scheduling, orchestrating and monitoring executions of data flows.

Partnership

Databricks offers services that simplify development and deployment processes in both the ETL and ML domains. We are talking about common tools for versioning, automating, scheduling and releasing code, as well as tools for monitoring executions, all wrapped up in a single platform. Databricks offers Databricks Repos that allow integration with common git providers, and Databricks Workflows that allow scheduling, orchestrating and monitoring of data flow executions.

Our expertise

Numerous projects successfully delivered into production
Active certifications
Databricks Certified Associate Developer for Apache Spark
Databricks Certified Data Engineer

Our consulting services

Initiation of new projects
Assessment of existing solutions and migration of data platforms
Design and implementation of data lake and lakehouse solutions
Design and implementation of data science solutions
Remote or in-house training

Use Cases

Data Infrastructure