Top 10 Informatica Big Data Parser [EOL] Alternatives

Name: Informatica Big Data Parser [EOL]
Brand: Informatica

Top Informatica Big Data Parser [EOL] Competitors

Discover the top alternatives and competitors to Informatica Big Data Parser [EOL] based on the interviews we conducted with its users. The top alternative solutions include Apache Spark, Cloudera Distribution for Hadoop, and HPE Data Fabric. The alternatives are sorted based on how often peers compare the solutions.

Informatica Alternatives Report

Learn what solutions real users are comparing with Informatica, and compare use cases, valuable features, and pricing.

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Apache Spark

4.2 out of 5

Apache Spark

4.2 out of 5

Apache Spark enables organizations to process and analyze large datasets with applications in security analytics, predictive modeling, ETL transformations, and real-time streaming. Valued for scalability and fault tolerance, its key features include SQL support, machine learning libraries, and language versatility. Users seek enhanced management tools, improved memory handling, and simplified configuration.

Pricing

Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.

Spark is an open-source solution, so there are no licensing costs.

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Pricing

Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.

Spark is an open-source solution, so there are no licensing costs.

Cloudera Distribution for Hadoop

4.0 out of 5

Cloudera Distribution for Hadoop

4.0 out of 5

Cloudera Distribution for Hadoop excels in big data management, providing analytics, storage integration, real-time processing, and machine learning capabilities. It supports industries like finance, telecom, and energy with features like Cloudera Manager, Impala, and enterprise security. The platform enhances data processing with its integration flexibility, scalability, and compatibility with tools like Hive and Pig.

Pricing

When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive.

The price could be better for the product.

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Pricing

When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive.

The price could be better for the product.

HPE Data Fabric

4.0 out of 5

HPE Data Fabric

4.0 out of 5

HPE Ezmeral Data Fabric enables data lake formation, facilitating large dataset consolidation. Organizations use it for data processing, Spark-powered distributed tasks, and NoSQL database functions. It provides multi-tenancy, security, and Docker integration. Users benefit from data reliability via replication, high availability, MapR-DB, and efficient POSIX-compliant file system performance across clusters.

Pricing

The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs.

There is a need for my company to pay for the licensing costs of the solution.

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Pricing

The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs.

There is a need for my company to pay for the licensing costs of the solution.

Spark SQL

3.9 out of 5

Spark SQL

3.9 out of 5

Spark SQL supports data engineering and analytics by handling big data and facilitating machine learning. Its strengths include efficient queries, SQL integration, data processing flexibility, and stable performance in distributed ecosystems. Users appreciate DataFrames and Hive interoperability but face challenges with Delta Lake compatibility, resource management, and educational resources. Improved integration with visualization tools is desired.

Pricing

The solution is open-sourced and free.

There is no license or subscription for this solution.

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Pricing

The solution is open-sourced and free.

There is no license or subscription for this solution.

Cask

1 out of 5

Cask

1 out of 5

Cask provides data integration and management tools catering to enterprise needs. It offers valuable features such as a scalable architecture and compatibility with multiple data sources. There is room for improvement in bug resolution and documentation quality, which can enhance user experience and streamline operations.

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Related categories

Hadoop

Compute Service

Java Frameworks