Azure Synapse Analytics is described as the former Azure SQL Data Warehouse, evolved, and as a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives users the freedom to query data using either serverless or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
$4,700
per month 5,000 Synapse Commit Units (SCUs)
HCL Actian Data Platform
Score 8.0 out of 10
Mid-Size Companies (51-1,000 employees)
The HCL Actian Data Platform (formerly Actian Avalanche) hybrid cloud data warehouse is a fully managed service that aims to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a lower cost than alternative solutions. Avalanche has built-in self-service data integration that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling users to migrate or offload applications and data to…
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Pricing
Azure Synapse Analytics
HCL Actian Data Platform
Editions & Modules
Tier 1
$4,700
per month 5,000 Synapse Commit Units (SCUs)
Tier 2
$9,200
per month 10,000 Synapse Commit Units (SCUs)
Tier 3
$21,360
per month 24,000 Synapse Commit Units (SCUs)
Tier 4
$50,400
per month 60,000 Synapse Commit Units (SCUs)
Tier 5
$117,000
per month 150,000 Synapse Commit Units (SCUs)
Tier 6
$259,200
per month 360,000 Synapse Commit Units (SCUs)
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Offerings
Pricing Offerings
Azure Synapse Analytics
HCL Actian Data Platform
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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Community Pulse
Azure Synapse Analytics
HCL Actian Data Platform
Considered Both Products
Azure Synapse Analytics
Verified User
Anonymous
Chose Azure Synapse Analytics
They're all part of the Microsoft Azure family, so they are not exactly competitors. They overlap in functionality, but they're targeted at different levels of customers. Azure Data Factory is an excellent stand-alone PaaS (included in Synapse Analytics) for writing, scheduling, …
When client is already having or using Azure then it’s wise to go with Synapse rather than using Snowflake. We got a lot of help from Microsoft consultants and Microsoft partners while implementing our EDW via Synapse and support is easily available via Microsoft resources and …
In comparing Azure Synapse to the Google BigQuery - the biggest highlight that I'd like to bring forward is Azure Synapse SQL leverages a scale-out architecture in order to distribute computational processing of data across multiple nodes whereas Google BigQuery only takes into …
Director, eCommerce Analytics and Digital Marketing
Chose Azure Synapse Analytics
Azure Synapse Analytics stacks up well against the competitors I mentioned above. Technically, Azure SQL Datawarehouse is an upgraded version of the Azure SQL Database. So, the choice to move from one to the other depends on the processing needs of your company. If you need …
We also looked at Oracle Data Warehouse as part of our short list of products to implement as a solution. Oracle's product turned out to have less support by way of easily accessible internet blogs. Oracle was also considerably more expensive and we would have needed to hire …
SQL Data Warehousing is much easier to manage if you already have SQL Server experience and analysts who are familiar with its interface. We are currently piloting using NoSQL and Hadoop type databases but it is difficult to get set up properly. Additionally, we have to …
Synapse, in comparison has its ups and downs against the competitors. However, where it excels, and builds it's markets is the cheaper costs (compared to Redshift), low code platforms and an in house solution that does not need you to leave the Synapse workspace for end to end …
Databricks is a complete product with new features constantly coming out. This can be both good or bad, with a lot of innovation comes a responsibility to keep your code and pipelines fresh.
Our team evaluated multiple platform as I mentioned above , but we stacks up Azure Synapse Analytics because : 1. Easy UI and Unified platform advantage 2. Tight integrations with MS ecosystem.
We didn’t actually choose Actian, it arrived as part of an acquisition, and really served its purpose both when it was used by the smaller firm we acquired as well as afterwards when we were extracting data and folding the company into our own data and analytics culture. The …
In terms of a well-suited scenario - the Azure Synapse can be used to capture data from multiple sources (especially from onPrem sources apart from Dataverse) and update the transformed data based on the given conditions (eg: refresh data based on the specified date/time ranges). Also, the transformed data can simply be transferred to Azure Data Lake for further processing by utilizing other analytics tools such as PowerBI.
VectorWise is suitable to be a departmental data mart database or an operational data store (ODS). It is not suitable for enterprise data warehouse database.
Keeping things "complicated, but simple"; [heterogeneous] data formats seen as just SQL tables to business experts used to use Power BI, Excel, and any other traditional SQL-oriented BI tools
Integration options using "Synapse pipelines", the application of ADFs
The greatly integrated solution of independent things (Spark MPP cluster, MPP SQL Servers, ADFs) - all sitting under one roof. Great job!
Integration with super-fast, globally replicated data. I really appreciate the integration of NoSQL databases (namely Core API and Mongo API under Cosmos DB) with purely batch-processed BI data
The support community was not as robust as you would find in a Mulesoft or Informatica environment. Given time and growth, it’s possible it will blossom, but for now it is minimal.
Training is always a big thing for us, and the tool was not expansive enough for us to implement our own internal training program. There was some online training, and we acquired an expert when we brought on the new company, but some additional training tools would have helped the tool grown its user base internally.
Not a lot to set it apart from the competition. Most of the features are available with other more established tools, but for a small company that maybe grew too quickly and needs to get its arms around many different data sources, I can see the appeal. Not really geared for larger firms.
With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools
As I said before, more training or greater visibility to training tools/options would be a plus. It’s easy to publish YouTube videos these days, I think they should make more of them.
Differentiation would help, there’s not a lot out there to drive you to buy the product if you are well informed in the market. If you know the market, you steer towards the large or trendy products. It’s a good product, but lost in the noise of the field I think.
Hitching the wagon to a major software brand (like Mule did to Salesforce) would help grow the user base, and thus increase the activity in the support community. More users also translates into product champions.
The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.
Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.
They're all part of the Microsoft Azure family, so they are not exactly competitors. They overlap in functionality, but they're targeted at different levels of customers. Azure Data Factory is an excellent stand-alone PaaS (included in Synapse Analytics) for writing, scheduling, and monitoring pipelines. Azure SQL Database (and all the Azure SQL family) is excellent for traditional, SQL-based data warehouses, especially if you're migrating from on-premises. Combined with Azure Data Factory (that can run SSIS packages), it's a perfect solution for a simple path to the cloud. Azure Databricks is effectively the only internal "competitor" to Synapse Analytics but targeted more to a "platform-agnostic" audience. On the other hand, Synapse is more of a proprietary mix of products that are more tightly related to Microsoft technologies.
We didn’t actually choose Actian, it arrived as part of an acquisition, and really served its purpose both when it was used by the smaller firm we acquired as well as afterwards when we were extracting data and folding the company into our own data and analytics culture. The included hundreds of pre-built connectors gave us lots of options, but in the end, we were just too large of a company to rely on the product and needed a big-name player to address our wide-ranging needs. Powerful for its size, but not sized enough to address big businesses.
It definitely has a positive impact on ROI. We are able to use it to generate MORE revenue through predictive analytics and pricing optimization.
Because of the SQL Data Warehouse design, we're able to set up some self service reporting tools which allow our users to generate reports ad hoc instead of having a full time employee creating these by hand.
Having visibility into the data is very useful for management to make good business decisions.
