Apache Spark

99 Ratings
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Score 8.6 out of 101

Informatica Enterprise Data Integration

46 Ratings
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Score 8.3 out of 101

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Likelihood to Recommend

Apache Spark

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.
Nitin Pasumarthy profile photo

Informatica Enterprise Data Integration

One of the main strengths of Informatica Enterprise Data Integration is its performance. If your business requirements include processing of huge volumes of data, then Informatica is strongly recommended over other tools. I would recommend Informatica over Talend.On the other hand, if your business primarily works on Apple machines, Informatica Enterprise Data Integration is a resounding NO. Running Informatica on a VM or a Remote Desktop completely kills its efficiency.
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Feature Rating Comparison

Data Source Connection

Apache Spark
Informatica Enterprise Data Integration
9.5
Connect to traditional data sources
Apache Spark
Informatica Enterprise Data Integration
10.0
Connecto to Big Data and NoSQL
Apache Spark
Informatica Enterprise Data Integration
9.0

Data Transformations

Apache Spark
Informatica Enterprise Data Integration
9.5
Simple transformations
Apache Spark
Informatica Enterprise Data Integration
10.0
Complex transformations
Apache Spark
Informatica Enterprise Data Integration
9.0

Data Modeling

Apache Spark
Informatica Enterprise Data Integration
8.0
Data model creation
Apache Spark
Informatica Enterprise Data Integration
8.2
Metadata management
Apache Spark
Informatica Enterprise Data Integration
8.8
Business rules and workflow
Apache Spark
Informatica Enterprise Data Integration
7.6
Collaboration
Apache Spark
Informatica Enterprise Data Integration
7.6
Testing and debugging
Apache Spark
Informatica Enterprise Data Integration
8.1

Data Governance

Apache Spark
Informatica Enterprise Data Integration
7.9
Integration with data quality tools
Apache Spark
Informatica Enterprise Data Integration
8.1
Integration with MDM tools
Apache Spark
Informatica Enterprise Data Integration
7.6

Pros

  • It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
  • It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
  • Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.
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  • Informatica is a complex, full-featured data integration tool. We use Informatica primarily to extract data from our ERP systems, transform, and load it into operational and dimensional data stores.
Robert Goodman profile photo

Cons

  • Consumes more memory
  • Difficult to address issues around memory utilization
  • Expensive - In-memory processing is expensive when we look for a cost-efficient processing of big data
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  • We have found that upgrading from version to version can be a bit clunky and complex.
  • Watch out for their audit department. They will hunt down development environments that are in use and try to back charge you for their repositories. Ensure your development environments are properly licensed.
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Likelihood to Renew

No score
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Informatica Enterprise Data Integration8.9
Based on 3 answers
Availability of options to integrate with application oriented databases like SAP, Salesforce etc.Product Support.Reliability.
hai Mani profile photo

Alternatives Considered

Apache Pig and Apache Hive provide most of the things spark provide but apache spark has more features like actions and transformations which are easy to code. Spark uses optimization technique as we can select driver program and manipulate DAG (Directed Acyclic Graph)Python can be used even for data transformations but it requires lot of coding compared to Spark and it is even so slow.
Kamesh Emani profile photo
While Talend offers a much more comfortable interface to work with, Informatica's forte is performance. And on that front, Informatica Enterprise Data Integration certainly leaves Talend in the dust. For a more back-end-centric use case, Informatica is certainly the ETL tool of choice. On the other hand, if business users would be using the tool, then Talend would be the preferred tool.
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Return on Investment

  • Positive: we don't worry about scale.
  • Positive: large support community.
  • Negative: Takes time to set up, overkill for many simpler workflows.
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  • Increased reporting strategy
  • Data discovery and profiling
  • Master data management
  • ata lineage and data archiving
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Pricing Details

Apache Spark

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

Informatica Enterprise Data Integration

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
Additional Pricing Details