Tap into a unique asset processing engine with an unrivaled price-value ratio. Scale automatically to thousands of nodes, automate your ETL processes, and bring all your data under one roof – with zero infrastructure management.
We can help you implement and scale the Databricks platform so you can get the best of flexible lakehouses and the reliability of warehouses. Create an open and unified platform for all your data, analytics, and AI workloads.
Perform SQL and BI workloads directly on your data lakes, create advanced visualizations, and unlock up to 12x better price-performance value. Our data warehouse consultants will help you break away from legacy warehouses to innovative, serverless-based storage.
Our client was looking for a new analytics architecture to store, process, and glean insights from complex construction projects.
Our engineers went with a serverless architecture to enable cost-effective real-time processing and delivered a tailored analytics suite. The result is a robust architecture that processes and updates 100s of files per data center per day.
Our team was hired to perform Azure cloud migration and facilitate real-time IoT sensor data processing and customer analytics.
We performed the end-to-end migration process and enabled machine learning analytics based on Azure Databricks. The result is a new architecture that produces accurate insights into the supply chain with real-time dashboards.
A chemical manufacturer needed a new architecture to look into the chemical composition of mixtures and monitor the sales dynamics.
Our engineering team transformed the unstructured production stack with Azure Databriks, delivered an advanced analytics solution, and set up real-time streaming in Power BI for better reporting.
Databricks business model is a unified analytics platform that helps you prepare, act on, and understand your data. It’s also one of the most popular cloud-based enterprise solutions that allow you to use a variety of data services to clean and prepare your insights, build and train machine learning models, and generate insightful reports. You can also use Databricks features to collaborate with others on your data projects.
Databricks was founded by the creators of Apache Spark, a powerful open-source data processing platform. The latter also powers the core of Databricks, while Databricks provides a cloud-based platform that makes it easy to use Spark for data processing, analysis, and machine learning.
The platform runs on the cloud and helps you clean, prepare, and analyze data. You can use Databricks to run on Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure Databricks.
While the platform can run on any of the aforementioned providers, Databricks is optimized for AWS. In fact, the company has an AWS-specific version of the platform that makes it easy to get started with AWS and Databricks. If you’re looking to use Databricks on another cloud provider, you can still do so, but you may need to set up and configure the platform yourself.
You can use it on any hosting platform, regardless of the cloud.
Azure and Databricks are both cloud-based platforms that can be used for data processing and analysis. They are both managed services, meaning that they take care of the infrastructure and management for you.
The main difference between them is that Azure is a general-purpose cloud platform while Databricks is designed specifically for analytics and machine learning. Databricks is built on top of Apache Spark, which is a powerful engine for large-scale data processing. Azure also has a Databricks service integrated with other Azure offerings that allows you to leverage data analytics and AI based on Apache Spark.