Spark Databricks Vs. Synapse Analytics
Here we are discussing the big beat down that is Spark Databricks Vs. Synapse Analytics.
Spark is a cool open source big data processing platform that can revolutionize everything we are doing in building analytics platforms. Here we are discussing the big beat down that is Spark Databricks Vs. Synapse Analytics.
Databricks is cross-platform, and that’s an important piece; if users build a ton of scripts out using data bricks, they can have the option to port that to amazon in the future. So they have quite close parity in terms of the two versions working across them; it has its runtime, so the guys in Databricks can contribute 70% to 80% of content that goes into the spark open source project comes from Databricks.
- They were released in 2016 (AWS). It’s a first-party service on Azure. Unlike other clouds, it is not an Azure marketplace or a third-party hosted service.
- AWS / Azure Cross-Platform
- Databricks Proprietary Runtime. And also allows its users to combine structured and unstructured data for analysis.
- Built by the inventory of Spark
- It is integrated seamlessly with Azure services.
- It enables the use of Azure Kafka as a streaming data source.
- Provides direct access to Azure Blob Storage and Azure Data Lake Store.
- It eliminates the need to maintain two separate sets of users in Databricks and Azure for user authentication.
- Workspace Features
- Delta Engine
Pros of Databricks
- Extremely versatile and scalable.
- Easily add streaming data.
- Not only applicable for data engineering: “unified analytics”.
- Interactive notebook experience.
- Cloud agnostic/open-source.
- Best option for Machine Learning workloads.
Cons of Databricks
- Steep learning curve.
- Not serverless.
- So-so Git integration.
- Longer time to value.
- Poor Service Principal support.
Azure Databricks Workspace
- User Management
- Jupyter Notebooks
- Cluster management
- Very similar to the vanilla spark, it is quite portable even though the actual spark instance.
- Manages data warehousing and analysis on big data.
- The SQL serverless functionality provided by Azure Synapse Analytics enables Data Analytics, Data Engineers, and Data Scientists.
- Data warehousing, Big Data analytics, Data integration, and visualization into a single environment.
Synapse Dedicated SQL Pools
- Massively parallel processing (MPP) system.
- In this model, data from tables are distributed across nodes, and the results are joined in the head or control node. It is a model that is completely optimized for large-scale loading of data and reporting.
- Separate compute and storage (Pay for them separately).
- It allows to pause or resume databases within minutes.
- It is built in advance security like connection security, authentication, authorization, and encryption.
Pros of Synapse
- Sort of familiar to SQL BI folks (but not the MPP part).
- Benefits from t-sql knowledge and database ALM experience.
- Mature tooling for a meta-drive generation.
- Database project and Git integration in VS.
Cons of Synapse
- The user usually has to prepare the data and store it in Azure Storage before loading it in the SQL pool.
- To use the data, the pool needs to be active.
- By nature, it has poor support for semi-structured or unstructured data.
- Poor XML / JSON support.
- The performance of PolyBase was, in our experiences quite poor.
- Poor advanced analytics/data science support.
- Poor streaming data support.
When to Use Synapse or Databricks?
|Ad-hoc data lake discovery by code.||Synapse and Databricks|
|SQL analyses and Data warehousing||Synapse|
|Same data, data scientists play via Spark and data analysts play via SQL and BI use power BI||Synapse|
|More ML / AI development, GPU intensive tasks||Databricks|
|Dependent tech is much on Data lake format / Spark||Databricks|
|In-built GIT based developer experience||Databricks|
Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.