I use Azure Synapse Analytics for ETL/Data Engineering flows, and I appreciate its ability to process large amounts of data, similar to Databricks or MS Fabric. I like the major connections it offers with Azure Data Lake and other Azure solutions, which aid in saving ingested data efficiently. Another aspect I enjoy is the ease of use with data pipelines, especially the low-code approach that allows me to create a prototype and basic ETL flow in minutes. Review collected by and hosted on G2.com.

I believe it needs to improve its git integration features; I don't like the fact that every change is a commit with no clear commit message. Faster Spark Pool start time. Better Git integration framework. Implementation similar to Databricks is the best. Also, it's impossible to review code and pipelines in Azure DevOps, since all the files are JSONs. Review collected by and hosted on G2.com.

is how it unifies data warehousing and big data into a single platform, making it easier to query and analyze large datasets efficiently. It integrates SQL for structured queries, Apache Spark for big data processing, and pipelines for seamless data ingestion and transformation. Review collected by and hosted on G2.com.

What I dislike about Azure Synapse Analytics is that the initial setup and configuration can be complex, often requiring extra expertise to get everything working smoothly. Debugging and monitoring can also feel limited, especially when managing large pipelines or Spark workloads. Review collected by and hosted on G2.com.

I use Azure Synapse Analytics primarily for building end-to-end data analytics solutions as it helps integrate data ingestion, transformation, storage, and analytics into a single unified platform. I love its unified experience which combines data engineering, big data processing, and enterprise data warehousing within a single workspace, significantly improving efficiency and reducing architectural complexity. I appreciate that I can switch between serverless SQL, dedicated SQL pools, and Spark environments seamlessly. The integration with data lakes and tools like Power BI is smooth, enabling faster insight generation. I value its scalability and performance optimization capabilities, along with the built-in pipeline orchestration suited for handling large-scale analytics workloads. I also appreciate how it integrates with the broader Azure ecosystem, like Azure Data Lake Storage and Azure Machine Learning, facilitating the building of end-to-end data pipelines. The flexibility, operational efficiency, and cost efficiency offered by serverless querying and on-demand scaling are truly beneficial. Review collected by and hosted on G2.com.

The learning curve can be steep, especially for teams that are new to distributed data processing or hybrid architectures involving SQL pools and Spark. Debugging complex pipeline failures can sometimes lack detailed error transparency, which increases troubleshooting time. Performance tuning in dedicated SQL pools also requires careful resource management and distribution design, which may not be intuitive for all users. Additionally, cost monitoring and optimization across serverless queries, Spark jobs, and dedicated pools can become complex if governance is not properly established. Review collected by and hosted on G2.com.

I like that all data is in one place, notebooks, code, tables, it is easy to use and the UI is nice. Review collected by and hosted on G2.com.

Sometimes there are small bugs. When I make changes to notebooks, I have to commit every time; otherwise I lose those changes. Also, when I run a notebook, it gets detected as a change in version control. Review collected by and hosted on G2.com.

Azure Synapse Analytics had a wonderful UI called Synapse Studio which makes it extremely easy to use. It is highly scalable which makes it easier to find insights of important data. It allows linking resources and working with scripts as well as spark notebooks. Its feature of integrating Spark pools makes data processing and analytics hassle-free. It has security that of Azure so there is no compromise on that too. All in all, it is a go-to cloud ETL and data analytics tool. Review collected by and hosted on G2.com.

There is nothing that I dislike about Synapse Analytics as such. Just an improvement that I would like to see is the addition of a drag and drop feature in its UI. Review collected by and hosted on G2.com.

It provides Deep integration of Azure Machine Learning, Cognitive Services, and Power BI

It has the big data analytical capabilities of spark and data integration to move data between both.

It is the overcome of Azure Data factory tool, it provides the independent storage and compute cluster.

It provides the SQL pools which are Dedicated SQL pools and Serverless SQL pools we can work according to data.

It provides Hybrid data integration by using this we can connect with on-prem SQL and cloud-based storage. It provides a complete solution.

It offers cloud data warehousing, dashboarding, and machine learning analytics in single workspace which makes Synapse more powerful than ADF.

It can ingest all types of data including Relation data and non-relation data and can see data in SQL.

It provides parallel processing which manages analytical workload and processes large volumes of data. Review collected by and hosted on G2.com.

Overall Synapse is okay, but there are some problems -

There is no way to migrate data from a Dedicated SQL pool to a Serverless SQL pool, which increases manual intervention and this can be hectic for large data. Review collected by and hosted on G2.com.

Unified Analytics Platform

Scalability and Performance

Advanced Analytics Capabilities

Security and Compliance

Cost Optimization

Integration with Azure Services Review collected by and hosted on G2.com.

Maturity of Features: As with any evolving technology, certain features or functionalities within Azure Synapse Analytics might still be maturing or undergoing improvements. This could result in occasional limitations or gaps in certain areas, requiring workarounds or alternative approaches. Review collected by and hosted on G2.com.

The best thing about Azure Synapse analytics platform is that it provides all service under a single umbrella which reduces dependency and also helps in manage all service at once Review collected by and hosted on G2.com.

There are many things to dislike about Azure Synapse analytics. One of the things are CTE doesn't support in SQL pool instance either of them serverless or dedicated Review collected by and hosted on G2.com.

What I most like about azure synapse analytics is it gives us end to end manages instance. Be it azure sql Or adf and any other functionality. We don't need to manage any connections they are all managed by synapse. Review collected by and hosted on G2.com.

The cost structure is difficult to understand. And the logging metrics are not easy to use. Integrated external 3rd party data sources this capability seems to be missing. Review collected by and hosted on G2.com.

SQL support, User Interface is quick to learn, Power BI Connectivity Review collected by and hosted on G2.com.

Limited SQL features available, Changes and updates can be given a more enhanced explanation Review collected by and hosted on G2.com.