Greenplum HD vs IBM InfoSphere BigInsights

Struggling to choose between Greenplum HD and IBM InfoSphere BigInsights? Both products offer unique advantages, making it a tough decision.

Greenplum HD is a Ai Tools & Services solution with tags like analytics, big-data, postgresql, parallel-processing.

It boasts features such as Massively parallel processing (MPP) architecture, Column-oriented storage, In-database analytics, In-database Python programming, SQL support, Hadoop integration, Cloud-native deployment and pros including Fast query performance on large datasets, Scales to petabyte-scale data volumes, Flexible deployment options - on-prem or cloud, Opensource and free to use, Supports standard SQL, Integrates with Hadoop ecosystem.

On the other hand, IBM InfoSphere BigInsights is a Ai Tools & Services product tagged with hadoop, big-data, analytics, unstructured-data.

Its standout features include Distributed processing of large data sets across clusters using Hadoop MapReduce, Supports variety of data sources like HDFS, HBase, Hive, text files, Web console for managing Hadoop clusters and jobs, Text analytics and natural language processing tools, Connectors for integrating with SQL and NoSQL databases, Enterprise security features like Kerberos authentication, Analytics tools like BigSheets and Big SQL, and it shines with pros like Scalable and flexible for analyzing large volumes of data, Supports real-time analysis with HBase integration, Simplified Hadoop management through web UI, Advanced analytics capabilities beyond just MapReduce, Integrates with existing data sources and BI tools, Mature enterprise software backed by IBM support.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Greenplum HD

Greenplum HD

Greenplum HD is an open-source data analytics platform that enables fast processing of big data workloads. It is based on PostgreSQL and provides massively parallel processing capabilities for analytics queries across large data volumes.

Categories:
analytics big-data postgresql parallel-processing

Greenplum HD Features

  1. Massively parallel processing (MPP) architecture
  2. Column-oriented storage
  3. In-database analytics
  4. In-database Python programming
  5. SQL support
  6. Hadoop integration
  7. Cloud-native deployment

Pricing

  • Open Source
  • Free

Pros

Fast query performance on large datasets

Scales to petabyte-scale data volumes

Flexible deployment options - on-prem or cloud

Opensource and free to use

Supports standard SQL

Integrates with Hadoop ecosystem

Cons

Complex installation and configuration

Requires expertise to tune and optimize

Limited ecosystem compared to commercial options

Not fully managed like cloud data warehouses


IBM InfoSphere BigInsights

IBM InfoSphere BigInsights

IBM InfoSphere BigInsights is a Hadoop-based software platform for analyzing large volumes of structured and unstructured data. It facilitates managing and analyzing Big Data.

Categories:
hadoop big-data analytics unstructured-data

IBM InfoSphere BigInsights Features

  1. Distributed processing of large data sets across clusters using Hadoop MapReduce
  2. Supports variety of data sources like HDFS, HBase, Hive, text files
  3. Web console for managing Hadoop clusters and jobs
  4. Text analytics and natural language processing tools
  5. Connectors for integrating with SQL and NoSQL databases
  6. Enterprise security features like Kerberos authentication
  7. Analytics tools like BigSheets and Big SQL

Pricing

  • Subscription-Based
  • Pay-As-You-Go

Pros

Scalable and flexible for analyzing large volumes of data

Supports real-time analysis with HBase integration

Simplified Hadoop management through web UI

Advanced analytics capabilities beyond just MapReduce

Integrates with existing data sources and BI tools

Mature enterprise software backed by IBM support

Cons

Can be complex to configure and manage

Requires expertise in MapReduce and Hadoop

Not fully open source unlike Hadoop

Can be expensive compared to open source Big Data platforms

Steep learning curve for developers new to Hadoop