Apache Hadoop vs. Azure Synapse Analytics

Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.

Incentivized

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.

Incentivized

• Handles large amounts of unstructured data well, for business level purposes
• Is a good catchall because of this design, i.e. what does not fit into our vertical tables fits here.
• Decent for large ETL pipelines and logging free-for-alls because of this, also.

Incentivized

• Create data pipelines to connect with multiple data workspace(s) and external data
• Ability to connect with Azure Data Lake (sequentially) for data warehousing
• Being able to manage connections and create integration runtimes (for onPrem data capture)

Incentivized

• Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates
• Not for small data sets
• Data security needs to be ramped up
• Failure in NameNode has no replication which takes a lot of time to recover

Incentivized

• It takes some time to setup a proper SQL Datawarehouse architecture. Without proper SSIS/automation scripts, this can be a very daunting task.
• It takes a lot of foresight when designing a Data Warehouse. If not properly designed, it can be very troublesome to use and/or modify later on.
• It takes a lot of effort to maintain. Businesses are continually changing. With that, a full time staff member or more will be required to maintain the SQL Data Warehouse.

Incentivized

Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free

Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.

Incentivized

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

We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.

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

Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.

Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which provides very high bandwidth to support MapReduce workloads.

Incentivized

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 blogs. I don’t see that with Snowflake

Incentivized

Basically, the billing is predictable, and this all about it.

Incentivized

• There are many advantages of Hadoop as first it has made the management and processing of extremely colossal data very easy and has simplified the lives of so many people including me.
• Hadoop is quite interesting due to its new and improved features plus innovative functions.

Incentivized

• We have had an improvement in our overall processing time
• Cost was much lower than most of its competitors
• Our reporting needs have grown and housing the data here has been great

Incentivized