Data1984
I wrote a summary where I compare Azure Synapse, Databricks and Azure Data Explorer focusing on the features that I find important. https://medium.com/@gorros/azure-synapse-databricks-and-azure-data-explorer-kusto-73a3a0339cf2
I decided to ask a ChatGPT following questions "What are the differences between Azure Synapse, Databricks, and Azure Data Explorer (Kusto)?" to compare with the review I wrote a while ago. Here is what I've got as an answer:
Azure Synapse, Databricks, and Azure Data Explorer (Kusto) are all Microsoft Azure services that are focused on data analytics and big data processing. While they have some similarities, there are also some key differences between the three.
Azure Synapse is a cloud-based data integration and analytics platform that combines enterprise data warehousing, big data integration and processing, and data integration and orchestration into a single platform. It is designed to help organizations quickly and easily integrate, process, and analyze large volumes of data from a wide range of sources.
Databricks is a cloud-based platform for data engineering, data science, and analytics. It is based on the open-source Apache Spark project, and it provides a managed environment for running Spark jobs, as well as tools for collaboration, data management, and machine learning.
Azure Data Explorer (Kusto) is a fast, highly scalable data exploration service that is optimized for log and time-series data. It is designed to help organizations quickly and easily analyze large volumes of data to gain insights and understand patterns and trends in their data.
Overall, each of these services has its own strengths and is suited to different use cases. Azure Synapse is a good choice for organizations that need a comprehensive data integration and analytics platform, while Databricks is a good choice for data engineering and data science workloads, and Azure Data Explorer is a good choice for analyzing log and time-series data.
Azure Synapse, Databricks, and Azure Data Explorer (Kusto) are all Microsoft Azure services that are focused on data analytics and big data processing. While they have some similarities, there are also some key differences between the three.
Azure Synapse is a cloud-based data integration and analytics platform that combines enterprise data warehousing, big data integration and processing, and data integration and orchestration into a single platform. It is designed to help organizations quickly and easily integrate, process, and analyze large volumes of data from a wide range of sources.
Databricks is a cloud-based platform for data engineering, data science, and analytics. It is based on the open-source Apache Spark project, and it provides a managed environment for running Spark jobs, as well as tools for collaboration, data management, and machine learning.
Azure Data Explorer (Kusto) is a fast, highly scalable data exploration service that is optimized for log and time-series data. It is designed to help organizations quickly and easily analyze large volumes of data to gain insights and understand patterns and trends in their data.
Overall, each of these services has its own strengths and is suited to different use cases. Azure Synapse is a good choice for organizations that need a comprehensive data integration and analytics platform, while Databricks is a good choice for data engineering and data science workloads, and Azure Data Explorer is a good choice for analyzing log and time-series data.
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https://aws.amazon.com/blogs/big-data/introducing-the-cloud-shuffle-storage-plugin-for-apache-spark/
Amazon
Introducing the Cloud Shuffle Storage Plugin for Apache Spark | Amazon Web Services
AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning (ML), and application development. In AWS Glue, you can use Apache Spark, an open-source, distributed processing system…
GitHub - Azure/ADX-in-a-Day: Hands on experience on Azure Data Explorer and Kusto Query Languages(KQL)
https://github.com/Azure/ADX-in-a-Day
https://github.com/Azure/ADX-in-a-Day
GitHub
GitHub - Azure/ADX-in-a-Day: Hands on experience on Azure Data Explorer and Kusto Query Languages(KQL)
Hands on experience on Azure Data Explorer and Kusto Query Languages(KQL) - Azure/ADX-in-a-Day
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Power BI vs Tableau: Which Should You Choose in 2023? | DataCamp
https://www.datacamp.com/blog/power-bi-vs-tableau-which-one-should-you-choose
https://www.datacamp.com/blog/power-bi-vs-tableau-which-one-should-you-choose
Datacamp
Power BI vs Tableau: Which is The Better Business Intelligence Tool in 2025?
Find out everything you need to know about Power BI vs Tableau, including the price, performance, UI, and more. Plus, find out how to learn each one here.
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Free data engineering zoomcamp starts on January 16.
GitHub
GitHub - DataTalksClub/data-engineering-zoomcamp: Data Engineering Zoomcamp is a free 9-week course on building production-ready…
Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼 - DataTalksClub/data-engineering-zoomcamp
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A new book by Andy Grove, creator of DataFusion, about query engines. DataFusion is an extensible query planning, optimization, and execution framework, written in Rust, that uses Apache Arrow as its in-memory format.
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AWS Lambdas - Python vs Rust. Performance and Cost Savings. - Confessions of a Data Guy
https://www.confessionsofadataguy.com/aws-lambdas-python-vs-rust-performance-and-cost-savings/
https://www.confessionsofadataguy.com/aws-lambdas-python-vs-rust-performance-and-cost-savings/
Confessions of a Data Guy
AWS Lambdas - Python vs Rust. Performance and Cost Savings. - Confessions of a Data Guy
Save money, save money!! Hear Hear! Someone on Linkedin recently brought up the point that companies could save gobs of money by swapping out AWS Python lambdas for Rust ones. While it raised the ire of many a Python Data Engineer, I thought it sounded like…
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Guide to Partitions Calculation for Processing Data Files in Apache Spark - DZone
https://dzone.com/articles/guide-to-partitions-calculation-for-processing-dat
https://dzone.com/articles/guide-to-partitions-calculation-for-processing-dat
DZone
Guide to Partitions Calculation for Processing Data Files in Apache Spark
Get to Know how Spark chooses the number of partitions implicitly while reading a set of data files into an RDD or a Dataset.
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Build a poor man’s data lake from scratch with DuckDB | Dagster Blog
https://dagster.io/blog/duckdb-data-lake
https://dagster.io/blog/duckdb-data-lake
dagster.io
Build a Data Lake with DuckDB + Dagster
Use DuckDB, Python, and Dagster to build a lightweight data lake with SQL transforms and Parquet file support.
Pandas 2.0 and its Ecosystem (Arrow, Polars, DuckDB) | Airbyte
https://airbyte.com/blog/pandas-2-0-ecosystem-arrow-polars-duckdb
https://airbyte.com/blog/pandas-2-0-ecosystem-arrow-polars-duckdb
Airbyte
Pandas 2.0 and its Ecosystem (Arrow, Polars, DuckDB) | Airbyte
Dive deeper into the power of Pandas and how leveraging it can benefit your organization. Explore a new way to work with data and unlock powerful insights!