Blog > Introduction to Databricks and Apache Spark
What Databricks Offers Beyond Apache Spark?
Apache Spark is an open-source project data management tool specifically for cluster computing with other features that come out of the box and make it a good data analytics engine. Specifically, Apache Spark descended from previous technologies such as Hadoop and MapReduce. It does many intermediate operations entirely in memory without writing their results back on disk, increasing the total speed of users’ processing. Besides mapping and reducing, it also processes SQL queries, streaming data, ML models, and graph calculation.
Databricks, as a comparison, is also a data analytics server, but this one is a managed service in the cloud. It is not only managed by the data analytics service; there are others like Stratio, but Databricks is famous specifically because its developers are also some of the original developers of Apache Spark. Databricks contains a modified Spark instance called Databricks runtime, which has some improvements and optimizations over base Spark both for normal processing and connections to the things. It connects to several external technologies and many internal tools, and it’s cloud-native on Azure and AWS.
Features Comparison Databricks and Apache Spark
Here we discuss a feature comparison of Databricks and Apache Spark and the difference between Databricks and Apache Spark.
- Better performance on the specific operation.
- Better connection to external technologies.
- Databricks runtime offers improved performance on some specific internal operations. Some general tweaks are supposed to improve general performance and better connections to some external technologies that are not within Spark. But Databricks is specifically a cloud service.
- The first external feature that Databricks specifically provides is notebooks.
- Notebooks generally are web-based interfaces for editing specific types of documents. So that users’ notebook files are made up of cells that can all contain a bunch of different types of data such as Code, Markdown, Images, Data visualization, and Interactive elements.
- The user code block is pretty standard; markdown blocks are also generally pretty standard. The user can embed any sort of markdown code. It also allows us to use images and data visualization if users’ code produces graphs.
- The users can run notebooks with Spark by themself.
- It comes integrated with Databricks by default.
Machine Learning Frameworks
- Both Spark and Databricks runtime include MLLib, Spark’s inbuilt machine learning framework.
- Developers are often more familiar with other specific machine learning frameworks.
- SciKit Learn, TensorFlow, Keras, PyTorch, etc.
- Databricks offers integration with a number of these machine learning frameworks.
- Some standalone tools for connecting these frameworks to Apache Spark.
MLflow / AutoML Frameworks
- Machine Learning management frameworks keep track of, automate and manage aspects of the machine learning process.
- Allows training many models of many algorithm types and parameter grids and keeps track of performance over the possible settings.
- Reproducible workflows.
- Deployment tools.
- Possible to manage by hand or use other ML-management frameworks.
BI Tool Integrations
- Databricks also helps to manage the arts of data analysis and visualization.
- Big part of existing business platforms.
- Easy with connectors.
- Data is visible in Databricks tables.
Delta Lake/Data Lakes
- Data lakes are cloud storage solutions that can store structures and unstructured data.
- AWS S3, Azure Data Lake Storage, GCP Cloud Storage, and Hadoop all can be used as a primary storage slash backup for users’ data.
- Delta lake acts as a layer on existing data lakes:
- Based on Apache Parquet data storage.
- Delta lake ensures ACID Transactions, schema enforcement, backup/restore.
- Both have all five sections, core, SQL, Streaming, MLlib, graph.
- Local and cloud deployment.
Author: SVCIT Editorial
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