We all have heard that big data has become a goldmine for businesses worldwide to make more informed business decisions, serve customers with the right products, and stay ahead of competitors. Billions of people now have access to the internet, and they are generating huge volumes of big data. Businesses across all industries are relying on this data to achieve better results.
Most big data come in unstructured formats, and according to research, 95 percent of businesses feel that managing big data is a complex task. The traditional data processing methods cannot handle this big data. Databricks is one of the new-age solutions for processing large volumes of data and gaining insights from it.
In this blog post, we will discuss one of the core aspects of Azure Databricks, which is a workspace. Databricks workspace offers a collaborative work environment wherein different teams can collaborate and perform end-to-end data processing operations.
Let's get into the details now:
Databricks is a cloud-based platform that is a one-stop solution for all your data processing needs. All your data engineering, data science, and machine learning teams can collaborate and work in a single environment. Compared to other solutions, databricks is fast, secure, cost-effective, and supports all cloud platforms. It offers some critical features for easy big data processing, including multi-language support, collaborative workspace, scalability, multi-source connectivity, etc.
Related Article: Databricks Tutorial
The primary purpose of using databricks is to process big data. Following are the typical uses of databricks:
Databricks Workspace was launched back in 2014 to build data science applications. It had limited features and did not support additional libraries, source files, etc. These things made Databricks develop the next version of Workspace 2.0, which will be released in 2020. It has solved the challenges of its first version and offers a collaborative environment for data teams.
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Azure Databricks Workspace is a runtime environment that provides a simple user interface for managing databricks assets such as Clusters, Notebooks, Jobs, Libraries, Repos, Models, Experiments, etc. The data teams can collaborate and perform operations like running data analytics, creating spark clusters, building pipelines, deploying machine learning models, scheduling workloads, and more.
Following are the different Databricks Assets you can access from the workspace UI. Workspace organizes all the assets into folders and provides computational resources.
Following are the key Databricks assets:
Clusters are unified computational resources, and data teams like data engineers, scientists, and machine learning professionals can use these clusters for various use cases, such as running analytics, pipelines, machine learning workloads, etc.
It is a web-based interface allowing developers to write and execute code. Notebook Cells facilitate the developers to work with files, add text, create narratives, build visualizations, etc. It acts as an interactive document that the authorized developers of an organization can access and update.
Databricks jobs enable users to execute operations on a scheduled basis. Jobs are a popular way to automate certain operations like model building, ETL, etc. A pipeline can consist of sequential jobs that run one after another to execute a specific task.
Databricks libraries allow developers to deploy third-party or locally written code to notebooks. It allows developers to install required libraries, and there are three types of libraries available:
Repos are databricks folders that provide integration with remote git repositories. Using Repos, developers can code in a notebook and sync it to the hosting providers.
A databricks model is registered in the MLflow model registry. The model registry is a centralized repository where the complete lifecycle of a databricks model is managed. It offers information such as modern lineage, present condition, model versioning, stage transition, annotations, and descriptions.
Related Article: Azure Data Factory vs Databricks
Conclusion:
So far in this article, we have discussed end-to-end aspects of Databricks workspace. Databricks Workspace offers a collaborative workspace for different teams, improves coordination and productivity, and offers the required resources for processing enormous amounts of data. Thanks for reading!
By Tech Solidity
Last updated on January 24, 2024
