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Databricks is a big data analytics platform that provides a collaborative environment for data scientists, engineers, and analysts to work with large-scale data processing and machine learning. It is built on top of Apache Spark, an open-source distributed computing system. Databricks offers a unified workspace that integrates various components for data ingestion, processing, analysis, and visualization. Below are some key components and features of Databricks:
1. Workspace: Databricks provides a collaborative
environment known as the Databricks Workspace, where users can create and
manage notebooks, clusters, and libraries. Notebooks are interactive documents
that can contain both code and rich text elements. - AzureData Engineer Online Training
2. Notebooks: Databricks notebooks are a key
feature, allowing users to write and execute code collaboratively and
interactively. Notebooks can contain code written in languages such as Python,
Scala, SQL, and R.
3. Clusters: Clusters in Databricks are computing
resources that can be provisioned to process data and run code. Users can
create and manage clusters with specific configurations to meet the
requirements of their workloads.
4. Libraries: Databricks support the use of
libraries, which are external packages or modules that can be added to the
environment. These can include Python libraries, Scala/Java libraries, and JAR
files. -Data Engineer Course in Hyderabad
5. Data Import and Integration: Databricks supports integration with
various data sources, including data lakes, databases, and streaming platforms.
It provides connectors and APIs for easy integration with popular data storage
systems.
6. Structured Streaming: Databricks supports real-time data
processing through Spark's Structured Streaming API. This allows users to
process and analyze streaming data in a structured manner.
7. Machine Learning: Databricks include machine learning
capabilities, allowing users to build, train, and deploy machine learning
models at scale. It supports popular machine learning frameworks such as MLlib,
TensorFlow, and Scikit-Learn. - AzureData Engineer Course
8. Collaboration: The collaborative nature of
Databricks Workspace enables multiple users to work on the same notebooks
simultaneously. It also provides version control for notebooks, making it
easier to track changes.
9. Visualization: Databricks supports various
visualization tools and libraries for creating charts and graphs within
notebooks. It also integrates with external visualization tools like Tableau.
10. Security and Governance: Databricks include features for
managing security and governance, including access controls, audit logging, and
integration with identity providers. - Data
Engineer Training Hyderabad
11. Community and Marketplace: Databricks has a vibrant community
where users can share code snippets, and best practices, and learn from each
other. The Databricks Marketplace also offers a variety of pre-built notebooks,
libraries, and connectors that users can leverage.
It's important to note that specific content and features in
Databricks may evolve, so it's recommended to refer to the official Databricks
documentation for the latest information. - Azure
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