Azure Synapse Analytics vs. Cloudera Enterprise Data Hub

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.

Incentivized

Cloudera is critical for constructing an organizational data center
while maximizing the value of that volume of data.



Cloudera is great for comprehending data and querying for valuable
replies.



Cloudera supports data transfer from a variety of external databases and
third-party platforms.

Incentivized

• The combination of SQL/unstructured data
• 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

Incentivized

• One of the oldest distributors of enterprise standard Hadoop.
• Distribution is based on open source Hadoop even though customizations are done on top of that.
• Faster updates and bug fixes to the products as they have Apache committers.
• Central configuration and control of your Hadoop platform (but still needs improvements).

Incentivized

• 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

Incentivized

• Not fully Open Source, couple of components of the distributions are privately owned, meaning with public contributions are not welcome
• Improvements to Cloudera manager can only be recommended. its very hard to get it done once recommended as the full control is with them.
• Should make components more aligned to Open Source rather than making it closed sourced.
• Custom Features of open source software tools supported only by Cloudera are tricky. Cant commit changes to tools like Hue.
• Improvements to Cluster Management tool is required, which are already available to its competitors.

Incentivized

Likely to renew the use in case the requirements for Cloudera remain valid. The rapid change in customer requirements and solutions that must be validated, integrated or tested changes. As the maturity of the solution increases, the requirements to renew use decrease. From a solution feature perspective by itself would probably grade 10.

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.

Incentivized

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.

Incentivized

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.

Incentivized

Cloudera is a
great choice because it provides fast streaming data for tracking, breaks down
silos by providing unified self-service platforms for data-driven insights,
secures machine learning, AI solutions, and stores self-service data, enabling
our analysts to concentrate on more important tasks like displaying critical
information.

Incentivized

• 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.

Incentivized

• Cloudera products are the most widely. It is more business friendly as data is more secure. The sensitive data that you operate on is local to you and your project rather than processing this data on Cloud.
• Cloudera is definitely faster as wait time is reduced if on Cloud.
• A lot range of products are covered. So it is definitely good for businesses and had good returns on investments.