Apache Spark is a popular unified analytics engine that has witnessed rapid adoption by companies across an expansive range of industries and sectors ever since its release. It has emerged as the largest open source community in big data and boasts of having more than 1000 contributors from beyond 250 organizations. Azure Databricks additionally contributes heavily to the Apache Spark project. Users need not configure the Apache Spark cluster, which includes security, storage, network, configuration, VM creation, and more. Databricks already has a cluster that is properly configured and ready to be used, allowing users to focus more on their business requirements and applications.
Capabilities and Features
Spark can run seamlessly on top of Azure Databricks. Users get the opportunity to freely scale up or down their cluster with the help of the ‘drag and drop’ feature. Some of the most useful components of Spark that can be found on top of Azure Databricks include:
- Streaming Capability
- GraphX for data exploration and cognitive analytics
- MLib for machine learning support
- Core API for integration with R, Python, Scala, Java, and SQL
- DataFrames with Spark SQL for working with structured data
Azure Databricks also allows the users to remove or terminate a Spark cluster if it is not used anymore. After creating a new cluster, the users shall have the option to specify after how many minutes of inactivity the cluster should be removed. This feature particularly comes as a huge help in the development phase and allows users to save a lot of resources without getting into complicated procedures. Having integration points with Azure SQL Data Warehouse, Azure Data Lake Store, Azure Blob Storage, Hadoop Storage, and Apache Kafka, this combination along with Spark capability to combine data streams with static data makes Apache Spark an ideal solution for swift deployment and reliable services.
Apache Spark provides incredible support for Fog Computing, Data Analysis and ML in IoT scenarios. It is used for streaming capabilities in many instances, where it allows the users to ETL on top of data streams. This triggers events based on stream content or data enrichment with static content whenever required. AI and ML capabilities inside Azure Spark go a long way in helping users to process and analyze data streams in real-time
Engineered from the bottom-up for performance, Apache Spark has the capacity to be 100x faster in comparison to Hadoop when it comes to large scale data processing by exploiting in-memory computing and other optimizations. It is pretty fast when data is stored on a disk as well. Azure Databricks offers 5x performance over open source Spark, collaborative notebooks, integrated workflows, and enterprise security.