Databricks and spark are two of the most widely used technologies in the fields of analytics and data science respectively. There are some significant distinctions between the two, even though both of them perform functions that are comparable to one another. Both databricks and spark have unique characteristics that set them apart from one another. Nevertheless, even though both products were developed to operate on the same engine and, in some instances, use comparable code as a point of reference, there are significant differences between them; some of these differences make one option a better choice than the other. This is to highlight these differences and help you decide which is best for you based on your specific use case or understanding of your requirements. This will help you decide which is best for you based on understanding your requirements.
MAIN DIFFERENCES BETWEEN DATABRICKS AND SPARK
| DATABRICKS | SPARK | |
| Features | Building on top of Spark, Databricks offers highly dependable and high-performing data pipelines and Productive data science at scale | Spark features give speed, simplicity of use, and breadth of use benefits and provide APIs enabling a range of use cases: Data integration and ETL, Interactive analytics, Machine learning, and advanced analytics, and Real-time data processing |
| Integrations | Compatible with common tools in the ecosystem | Not compatible with all common tools |
| Pricing | Costs as little as $0.07/DBU per month | Apache Spark is open-source. You have to pay only when you use any bundled product, Apache Spark is open-source. You have to pay only when you use any bundled product |
| Support | Support that is Unmatched by Any Other Provided by the Top Committers. | Does not match databricks support. |
DATABRICKS VS SPARK: FEATURES AND CAPABILITIES
KEY FEATURES OF DATABRICKS
- Optimized environment: provides a maintenance-free cloud infrastructure, managed Spark clusters, and an interactive workspace for visualization and exploration.
- Interactive workspace: helps data engineers, data scientists, and business analysts boost productivity and collaboration
- Runtime: allows data scientists to iterate quickly as a team by reducing the requirement for specialized expertise to set up and manage data infrastructure
- Integration of Machine Learning: can store machine learning processes, models, and experiments and quickly and easily find and share key insights using Power BI.
KEY FEATURES OF SPARK
The following are some of the characteristics of Spark that contribute to its status as one of the most widely used Big Data platforms:
- Data Access in a Unified Format: It essentially supports a standard method for accessing a wide variety of data sources, such as Hive, Avro, Parquet, ORC, JSON, and JDBC, among others. In addition to that, we can combine the information from all of these sources. However, this is a very helpful step to take to accommodate all of the users who are already utilizing Spark SQL.
- Scalability: Spark utilizes the benefit of the RDD model. In a nutshell, it allows for fault tolerance in the middle of queries and large jobs. Additionally, the same engine is used for both interactive and long queries when using this system.
- A high degree of coherence: When it comes to running unmodified Hive queries in Spark, we are permitted to do so on existing warehouses. Additionally, it offers complete compatibility with the Hive data, queries, and UDFs that are already in use. In addition, a complete rewrite of both the MetaStore and the Hive frontend is performed.
- Integration: In general, Spark SQL and Spark programs can work together. Spark SQL also lets us ask questions about structured data in Spark programs. But it is possible to do so by using SQL or a Java or Scala DataFrame.
- Connecting to the Standard: Basically, Spark SQL is easy to connect to JDBC or ODBC. But for business intelligence tools, both turned out to be the norm in the industry. So, it has JDBC and ODBC connectivity with server mode, which is industry standards.
- Hive Tables Processing: SparkSQL can be used to run batch processing on Hive tables.
- Query Optimization: In SparkSQL, when it comes to querying, the optimization engine does most of the work. It turns each SQL query into a plan that makes sense. Also, it can be turned into many plans for physical execution. But at the time of execution, it picks the physical plan that is best for the whole plan. Also, it ensures fast execution of HIVE queries.
DATABRICKS VS SPARK PRICING
Databricks Pricing
Databricks cost as little as $0.07 per month. There are 6 different plans for Databricks:
Data Engineering Light: $0.07/DBU per month.
Standard: $0.07/DBU per month.
Premium: $0.10/DBU per month.
Enterprise: $0.13/DBU per month.
Data Engineering: $0.15/DBU per month.
Data Analytics: $0.40/DBU per month.
*DBU: Databricks Unit.
Spark Pricing
Apache Spark is open-source. You have to pay only when you use any bundled product.
REASONS TO CHOOSE DATABRICKS OVER SPARK
- Python, R, and SQL are only some of the common programming languages that may be utilized with Azure Databricks, even though it is based on Spark.
- Databricks make it easy to increase productivity and collaboration.
- It offers simple integration with the entirety of Microsoft’s product line
- Databricks can readily connect to a wide variety of sources, including on-premise SQL servers, CSVs, and JSON files.
- Azure Databricks is suitable for both less significant tasks and enormous workloads.
- It offers extensive documentation and customer support.
REASONS TO CHOOSE SPARK OVER DATABRICKS
- Spark includes APIs that are simple to work with, and can be used to process massive datasets.
- Speed: When it comes to Big Data, the speed of processing is always something that matters. Because of Spark’s lightning-fast processing power, it has garnered a massive following among data scientists.
- Advanced Analytics: Spark not only supports ‘MAP’ and ‘reduce,’ but it also supports many other operations. In addition to that, it is compatible with Machine Learning (ML), Graph Algorithms, Streaming Data, SQL Queries, and so on.
- Spark possesses a lot of power: Because of its ability to process data in memory with low latency, Apache Spark is capable of handling a wide variety of analytical challenges. It comes equipped with well-built libraries for both machine learning and graph analytics algorithms.
- Community of open-source developers: Spark’s strength lies in the fact that it is supported by a sizable open-source developer community.
- Increased access to big data: Spark is making big data more accessible by offering up a variety of new options for it.
SIMILARITIES BETWEEN DATABRICKS AND SPARK
- Both help in data analytics
- Both offer data integration
- Both offer real time data analysis
DATABRICKS AND SPARK ALTERNATIVES
- Snowflake: Snowflake removes the administrative and management responsibilities of traditional data warehouses and large data systems. Snowflake is a pure data warehouse as a service operating on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to adjust.
- Domino: You can use their infrastructure that is hosted in the cloud to safely execute your code on powerful hardware with just one command – all without making any modifications to your code. If you have your infrastructure, you can take advantage of the robust and user-friendly cluster administration capability that their Enterprise service offers behind your firewall.
- Confluent: It is a data streaming platform that is built on Apache Kafka. It is a full-scale streaming platform, meaning that in addition to publish-and-subscribe functionality, it can also store and process data within the stream itself.
- Azure HDInsight: It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
DATABRICKS VS SPARK: FINAL VERDICT
Databricks is integrated with a wide variety of internal and external technologies, all of which contribute additional functionality to the platform. Databricks is a more capable analytics engine than Apache Spark on its own due to features such as the administration of connections to data lakes and other machine learning frameworks. Some of these connections and adjustments are things that can be recreated without the use of Databricks, and this idea may serve as the foundation for a series of other postings in the near or far future.
FREQUENTLY ASKED QUESTIONS
Do Databricks run on AWS?
Databricks runs on AWS and integrates with all of the major services you use like S3, EC2, Redshift, and more. In this demo, we’ll show you how Databricks integrates with each of these services simply and seamlessly to enable you to build a lakehouse architecture.
Can Spark be used for ETL?
Spark supports Java, Scala, R, and Python. It is used by data scientists and developers to rapidly perform ETL jobs on large-scale data from IoT devices, sensors, etc. Spark also has a Python DataFrame API that can read a JSON file into a DataFrame automatically inferring the schema.
Is Databricks an ETL?
ETL (Extract, Transform, and Load) is a Data Engineering process that involves extracting data from various sources, transforming it into a specific format, and loading it to a centralized location (majorly a Data Warehouse). One of the best ETL Pipelines is provided by Databricks ETL.
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