159 Reviews and Ratings
274 Reviews and Ratings
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Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.Incentivized
Your upcoming app can be built faster on a fully managed SQL database and can be moved into Azure with a few to no application code changes. Flexible and responsive server less computing and Hyperscale storage can cope with your changing requirements and one of the main benefits is the reduction in costs, which is noticeable.Incentivized
Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.Apache Spark does a fairly good job implementing machine learning models for larger data sets.Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.Incentivized
Maintenance is always an issue, so using a cloud solution saves a lot of trouble.On premise solutions always suffer from fragmented implementations here and there, where several "dba's" keep track of security and maintenance. With a cloud database it's much easier to keep a central overview.Security options in SQL database are next level... data masking, hiding sensitive data where always neglected on premise, whereas you'll get this automatically in the cloud.Incentivized
Memory management. Very weak on that.PySpark not as robust as scala with spark.spark master HA is needed. Not as HA as it should be.Locality should not be a necessity, but does help improvement. But would prefer no localityIncentivized
One needs to be aware that some T-SQL features are simply not available.The programmatic access to server, trace flags, hardware from within Azure SQL Database is taken away (for a good reason).No SQL Agent so your jobs need to be orchestrated differently.The maximum concurrent logins maybe an unexpected problem.Sudden disconnects.The developers and admin must study the capacity and tier usage limits https://docs.microsoft.com/en-us/azure/azure-subscription-service-limits otherwise some errors or even transaction aborts never seen before can occur.Only one Latin Collation choice.There is no way to debug T-SQL ( a big drawback in my point of view).Incentivized
Capacity of computing data in cluster and fast speed.
This is best solution as a DBA one could expect from a service provider and as a cloud service, it removes all your hassles.Incentivized
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.Incentivized
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
We give the support a high rating simply because every time we've had issues or questions, representatives were in contact with us quickly. Without fail, our issues/questions were handled in a timely matter. That kind of response is integral when client data integrity and availability is in question. There is also a wealth of documentation for resolving issues on your own.Incentivized
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python Incentivized
We moved away from Oracle and NoSQL because we had been so reliant on them for the last 25 years, the pricing was too much and we were looking for a way to cut the cord. Snowflake is just too up in the air, feels like it is soon to be just another line item to add to your Azure subscription. Azure was just priced right, easy to migrate to and plenty of resources to hire to support/maintain it. Very easy to learn, too.Incentivized
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.Incentivized
Perfect for small and medium databases, being very cost effective.As a Platform as a Service, there is no concern about patches, upgrades and end of life.Be aware of security and network capabilities. The service cannot run in the VNET as Azure Virtual Machines do.