Wednesday, November 8, 2023

Data Engineering — Azure Databricks or Azure Synapse Analytics

 The cloud is the fuel that powers today’s digital companies, with businesses paying solely for the specific services or resources that they consume over time.

Azure Synapse Analytics bridges the gap between these two worlds by providing a uniform experience for ingesting, preparing, managing, and serving data for instant BI and machine learning needs.


Databricks is ideal for the “processing” layer, whereas Azure Synapse Analytics is ideal for the serving layer due to access control, active directory integration, and interaction with other Microsoft products.


Azure Databricks and Azure Synapse Analytics are mostly used for machine learning, and Synapse is also a Data Warehouse, therefore it is optimised for OLAP.

Azure Synapse Analytics vs Azure Databricks

Apache Spark powers both Databricks and Synapse Analytics. With optimized Apache Spark support, Databricks allows users to select GPU-enabled clusters that do faster data processing and have higher data concurrency.

Azure Synapse Analytics
 is an umbrella term for a variety of analytics solutions. It is a combination of Azure Data Factory, Azure Synapse SQL Pools (essentially what was formerly known as Azure SQL Data Warehouse), and some added capabilities such as serverless Spark clusters and Jupyter notebooks, all within a browser IDE interface.

Azure Synapse architecture comprises the Storage, Processing, and Visualization layers. The Storage layer uses Azure Data Lake Storage, while the Visualization layer uses Power BI.


Azure Synapse Pipelines is a lightweight orchestrator and are ideal for basic extract-load procedures that require highly parameterized copy actions with ADLS2 or specialised SQL pool integration.

Some ideal features of Azure Synapse Analytics

  1. Azure Synapse offers cloud data warehousing, dashboarding, and machine learning analytics in a single workspace.
  2. It ingests all types of data, including relational and non-relational data, and it lets you explore this data with SQL.
  3. Azure Synapse uses massive parallel processing or MPP database technology, which allows it to manage analytical workloads and also aggregate and process large volumes of data in an efficient manner.
  4. It is compatible with a wide range of scripting languages like Scala, Python, .Net, Java, R, SQL, T-SQL, and Spark SQL.
  5. It facilitates easy integration with Microsoft and Azure solutions like Azure Data Lake, Azure Blob Storage, and more.
  6. It includes the latest security and privacy technologies such as real-time data masking, dynamic data masking, always-on encryption, Azure Active Directory authentication, and more.

Azure Synapse is an unrestricted analytics solution that combines business data warehousing and Big Data analytics. It allows you to query data on your own terms, leveraging serverless on-demand or provided resources — at scale.

Azure Databricks uses mostly open-source software and utilizes cloud companies’ compute and storage costs. Databricks would be that it integrates more easily into the Azure ecosystem, and it is substantially more streamlined and works right away. It is now fully knob-free, yet given what it can accomplish, it takes very little configuration.


Databricks on the other hand is a complete ecosystem build cloud native. It supports that you write SQL, Python, R and Scala. It was built by the founders of spark and comes with tools that improve sparks capability e.g., in query performance and speed, Delta Lake for a lake format with version control and possibility to clone data between environments.


Databricks architecture is not entirely a Data Warehouse. It accompanies a LakeHouse architecture that combines the best elements of Data Lakes and Data Warehouses for metadata management and data governance.


Azure Databricks offers streamlined workflows and an interactive workspace for collaboration between data scientists, data engineers, and business analysts.


Some ideal features of Azure Databricks — Databricks has a lot more customizability and they have some internal libraries that are useful for data engineers. For instance, Mosaic is a useful geospatial library that was created inside Databricks. There are some ideal features inside Databricks as given below-

  1. Databricks are more open and the types of features they are releasing cover most of the things such as data governance, security, change data capture.
  2. Databricks uses Python, Spark, R, Java, or SQL for performing Data Engineering and Data Science activities using notebooks.
  3. Databricks has AutoML, and instead of a black box at the end for inference, you receive a notebook with the code that built the model you want.
  4.  AutoML is a wonderful starting point for most ML use cases that can subsequently be customised; it’s also ideal for learning and transforming “citizen” Data Scientists into coding practitioners.
  5. Databricks compute will not auto-start, which means you have to leave the clusters running to be able to allow users to query DB data.
  6. Databricks has a CDC option on their tables where it allows you to track changes. You can use this feature to get a list of rows that changed on the Trillion row Delta table (~billion a day).
  7. Databricks is generally cheaper (cost for X performance), so it’s easier to keep a shared auto scaling cluster running in Databricks.
  8. Databricks provides a platform for integrated data science and advanced analysis, as well as secure connectivity for these domains.


Databricks also have Delta sharing which is an interesting idea around making it easier to integrate with your lake house. The biggest selling point for us is that Databricks has understood that data platforms today is about machine learning and advanced analytics.


In an ideal business scenario, you (data engineers) can use Databricks to build data pipelines to process data and save everything as delta tables in ADLSG2, then use Synapse Analytics serverless pool to consume those delta tables for further data analysis and reports.


Azure Synapse and Databricks support Notebooks that help developers to perform quick experiments. Synapse provides co-authoring of a notebook with a condition where one person must save the notebook before the other person observes the changes.


However, Databricks Notebooks support real-time co-authoring along with automated version control.


Azure Synapse Analytics over Azure Databricks

  1. Azure Synapse Analytics is less costly to process than Azure Databricks when Spark Pools are used appropriately. Databricks are expensive, yet they give benefits that most people will never have.
  2. You pay per query/GB with Azure Synapse serverless. Because of this, serverless computing is charged per query, Synapse appears to be a suitable fit.
  3. If you are planning to do machine learning and data science later than go with synapse. Synapse is like Databricks, data factory, and SQL Server in one place.

 Azure Databricks over Azure Synapse Analytics

  1. Databricks comes with what can be seen as Spark improved with multiple optimizations which can perform x 50 times better. They have built Phonton which will always outperform Spark.
  2. Azure Synapse has no versions control in notebooks as where Databricks have this.


Microsoft Azure Synapse Analytics is a scalable, cloud-based data warehousing solution and includes business intelligence, data analytics, and machine learning solutions for both relational and non-relational data.


Azure Databricks is a large data analytics service built on Apache Spark that is quick, simple, and collaborative. It is intended for data science and data engineering. It is designed to store all of your data on a single, open LakeHouse while also unifying all of your analytics and AI workloads.


We propose that you assess your requirements and select:

  1. If you want a lot of product knobs at the sacrifice of productivity, use Synapse. To be clear, Azure Synapse Analytics is a collection of goods under the same umbrella. It’s like IBM Watson in terms of data processing.
  2. If you want a more refined experience at the sacrifice of certain capabilities, choose Azure Databricks. Databricks, for example, does not provide a no-code ML, although AzureML does.
  3. If you want to construct pipelines without writing code, Azure Synapse Analytics is a no-brainer.
  4. Use Azure Databricks for sophisticated analytics, large amounts of data processing, machine learning, and notebooks.

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