Databricks
Databricks offers a Spark-optimized analytics platform tailored to the workflows of data teams, integrating engineering, science and business roles collaboratively. Databricks provides a secure, collaborative, cloud-based platform optimized for Lakehouse architecture that enables users to unify data engineering, science, and analytics in extensive data sets integrated across AWS, Azure, and Google Cloud data object stores and services.
Key Capabilities
- Unified workspaces for engineering, science and business
- Optimized open-source Spark environment
- Notebooks promote automation and sharing
- MLflow addresses the entire machine-learning lifecycle
Benefits
- Simplifies Spark implementations through managed service
- Integrates skill sets across data roles on one platform
- Accelerates adoption of other automation tools like Koalas and MLflow.
- Improves collaboration across the analysis spectrum
Use Cases
- Blackrock automated complex investment analytics by unifying data teams onto Databricks’ collaborative data platform, strengthening risk management.
- Comcast built an automated pipeline analyzing viewer engagement data, helping to recommend particular movie genres and increasing viewership.
- ViacomCBS runs big data workloads on Databricks to automatically encode and tag +40K assets/day through Spark automation.
Top 15 Automation Tools for Data Analytics
The exponential growth in data in recent times has made it imperative for organizations to leverage automation in their data analytics workflows. Data analytics helps uncover valuable insights from data that can drive critical business decisions. However, making sense of vast volumes of complex data requires scalable and reliable automation tools.
In this article, we will be discussing the Top 15 Automation Tools Data Analytics teams rely on to efficiently collect, process, analyze, and visualize data. We explore each tool’s core capabilities, benefits, and real-world use cases across organizations. Let’s get started!
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