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 5000 Synapse Commit Units (SCUs)
TimeXtender
Score 9.0 out of 10
N/A
TimeXtender was designed to be a holistic solution for data integration that empowers organizations to build data solutions 10x faster using metadata and low-code automation.
$1,600
per month
Pricing
Azure Synapse Analytics
TimeXtender
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)
No answers on this topic
Offerings
Pricing Offerings
Azure Synapse Analytics
TimeXtender
Free Trial
No
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
—
On-Demand pricing is pay as you go, month-to-month, with no commitment, at the "on-demand" price of $3.33/credit.
It's well suited for large, fastly growing, and frequently changing data warehouses (e.g., in startups). It's also suited for companies that want a single, relatively easy-to-use, centralized cloud service for all their data needs. Larger, more structured organizations could still benefit from this service by using Synapse Dedicated SQL Pools, knowing that costs will be much higher than other solutions. I think this product is not suited for smaller, simpler workloads (where an Azure SQL Database and a Data Factory could be enough) or very large scenarios, where it may be better to build custom infrastructure.
TimeXtender has worked really well with our customers who have different data sources using complex data types in large quantities requiring a DW-like solution that can consolidate all data sources at one-HUB. TimeXtender does this well, and provides automation capabilities, the ability to easily handle slowly changing dimensions, handing data lineage and data security very well. TimeXtender has the ability to be very customizable, allowing the HUB to grow as your business does. TimeXtender's customer support team is super helpful and will work with you throughout your implementation to make sure you reach success with the product. The ROI for timeXtender versus competing products (there aren't many that do what timeXtender does) shows the investment to be worthwhile for the majority of organizations in today's data-rich corporate world.
Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.
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
Product Marketing: As implementers and resellers of this technology, we loved it. But, convincing clients who had not previously heard of TX/Discovery Hub was more difficult than it could have been if the company had a larger marketing force behind it.
Relatively New to Market: it creates a learning curve for early implementers.
More information should be published on timeXtender's website about product lines, including testimonials.
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.
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 account computation and storage.
For our clients, timeXtender was a much better solution. It offered a more cost-effective solution, easier integration, and better customer support for our complex client needs. The timeXtender team worked with us throughout the process to make sure we could create a success story that was repeatable for our clients, and they proved great partners.
Licensing fees is replaced with Azure subscription fee. No big saving there
More visibility into the Azure usage and cost
It can be used a hot storage and old data can be archived to data lake. Real time data integration is possible via external tables and Microsoft Power BI
