What users are saying about
127 Ratings
208 Ratings
127 Ratings
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Score 8.7 out of 100
208 Ratings
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Score 8.3 out of 100

Likelihood to Recommend

Apache Spark

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
Thomas Young | TrustRadius Reviewer

SSIS

  • SSIS is particularly well suited for jobs that need to be consistent, repeatable, and error managed.
  • Ongoing extract, transform, load [ETL] jobs that are scheduled or manual.
  • One-time ETL with complex datasets.
  • Migrations of large datasets.
SSIS is not well suited for small or simple datasets that can be copied or exported safely to flat files for import. It is possible to do this but would generally take longer to build in SSIS unless there was a good reason to .remove manual handling of the data in transport or the action needed to be testable/repeatable.
Anonymous | TrustRadius Reviewer

Feature Rating Comparison

Data Source Connection

Apache Spark
SSIS
8.6
Connect to traditional data sources
Apache Spark
SSIS
9.4
Connecto to Big Data and NoSQL
Apache Spark
SSIS
7.8

Data Transformations

Apache Spark
SSIS
9.2
Simple transformations
Apache Spark
SSIS
9.8
Complex transformations
Apache Spark
SSIS
8.6

Data Modeling

Apache Spark
SSIS
7.5
Data model creation
Apache Spark
SSIS
7.7
Metadata management
Apache Spark
SSIS
7.5
Business rules and workflow
Apache Spark
SSIS
8.2
Collaboration
Apache Spark
SSIS
7.0
Testing and debugging
Apache Spark
SSIS
7.6
feature 1
Apache Spark
SSIS
7.0

Data Governance

Apache Spark
SSIS
8.3
Integration with data quality tools
Apache Spark
SSIS
8.7
Integration with MDM tools
Apache Spark
SSIS
7.8

Pros

Apache Spark

  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
Nitin Pasumarthy | TrustRadius Reviewer

SSIS

  • SSIS works very well pulling well-defined data into SQL Server from a wide variety of data sources.
  • It comes free with the SQL Server so it is hard not to consider using it providing you have a team who is trained and experienced using SSIS.
  • When SSIS doesn't have exactly what you need you can use C# or VBA to extend its functionality.
Anonymous | TrustRadius Reviewer

Cons

Apache Spark

  • 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 locality
Anson Abraham | TrustRadius Reviewer

SSIS

  • SSIS memory usage can be quite high particularly when SSI and SQL server are on the same machine
  • SSIS is not available on any environment other than Microsoft Windows
  • SSIS does not function with any database engine back-end other than Microsoft SQL Server
Anonymous | TrustRadius Reviewer

Likelihood to Renew

Apache Spark

No score
No answers yet
No answers on this topic

SSIS

SSIS 10.0
Based on 3 answers
Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
Luca Campanelli | TrustRadius Reviewer

Usability

Apache Spark

Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.
Partha Protim Pegu | TrustRadius Reviewer

SSIS

SSIS 9.2
Based on 8 answers
If you use any of the Microsoft tools, it is easy to get using and understand since it uses Visual Studio and SSMS to work with. There are extensions you can get and some of those can be difficult to work with but usually they slide in nicely. The one thing to understand though is there is some installation and configuration on the SQL Server side that can take some time.
Steven Gockley, MBA, MCSA | TrustRadius Reviewer

Performance

Apache Spark

No score
No answers yet
No answers on this topic

SSIS

SSIS 8.7
Based on 6 answers
You can, of course, accidentally make sloppy code or workflows but it’s not a tools issue if you run packages in an inefficient way. With a company of our size, we never had any performance issues but I can imagine for larger companies that this will have a greater focus.
Anonymous | TrustRadius Reviewer

Support Rating

Apache Spark

Apache Spark 8.2
Based on 6 answers
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.
Yogesh Mhasde | TrustRadius Reviewer

SSIS

SSIS 8.1
Based on 8 answers
The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
Chris Morgan | TrustRadius Reviewer

Implementation Rating

Apache Spark

No score
No answers yet
No answers on this topic

SSIS

SSIS 10.0
Based on 1 answer
The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
Luca Campanelli | TrustRadius Reviewer

Alternatives Considered

Apache Spark

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
Anonymous | TrustRadius Reviewer

SSIS

Look for where you are going to load the data and from where are you extracting it. A lot of those who sell you the DB already have tools for ETL. I have heard good things at an enterprise level from great colleagues about Informatica, but honestly, I prefer SSIS.Although I did like TIBCO Jaspersoft and it can be mixed with TIBCO Spotfire for data analysis. If your ERP is SAP, then you must keep in mind using SAP BusinessObjects. As for free tools, and if you have a great and constant team of scripting (Perl, Python) engineers, then you can think about going open source.
Jose Pla | TrustRadius Reviewer

Return on Investment

Apache Spark

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Carla Borges | TrustRadius Reviewer

SSIS

  • I use SSIS to automate tasks that I'm repeatedly asked to do. "Hey, can you go into the system and close any open orders that we've fully filled?" Sure....then I schedule a package to do that for me every hour so I'm never asked again. It saves me time, which gives value to the company.
  • It removes the risk of human error. When people build files and send them, there's the risk that it doesn't happen the same way every time or gets forgotten. With SSIS, you spend some up front time building a process, but then you deploy it and forget it (unless it emails you that there was an error. You are putting error handling in your ETL, right?). Very repeatable and consistent business solution.
Greg Goss | TrustRadius Reviewer

Pricing Details

Apache Spark

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

SSIS

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Rating Summary

Likelihood to Recommend

Apache Spark
8.6
SSIS
8.9

Likelihood to Renew

Apache Spark
SSIS
10.0

Usability

Apache Spark
8.7
SSIS
9.2

Performance

Apache Spark
SSIS
8.7

Support Rating

Apache Spark
8.2
SSIS
8.1

Implementation Rating

Apache Spark
SSIS
10.0

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