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features
design
support

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Write a Review

Description

Iceberg is an advanced format designed for managing extensive analytical tables efficiently. It combines the dependability and ease of SQL tables with the capabilities required for big data, enabling multiple engines such as Spark, Trino, Flink, Presto, Hive, and Impala to access and manipulate the same tables concurrently without issues. The format allows for versatile SQL operations to incorporate new data, modify existing records, and execute precise deletions. Additionally, Iceberg can optimize read performance by eagerly rewriting data files or utilize delete deltas to facilitate quicker updates. It also streamlines the complex and often error-prone process of generating partition values for table rows while automatically bypassing unnecessary partitions and files. Fast queries do not require extra filtering, and the structure of the table can be adjusted dynamically as data and query patterns evolve, ensuring efficiency and adaptability in data management. This adaptability makes Iceberg an essential tool in modern data workflows.

Description

A Kudu cluster comprises tables that resemble those found in traditional relational (SQL) databases. These tables can range from a straightforward binary key and value structure to intricate designs featuring hundreds of strongly-typed attributes. Similar to SQL tables, each Kudu table is defined by a primary key, which consists of one or more columns; this could be a single unique user identifier or a composite key such as a (host, metric, timestamp) combination tailored for time-series data from machines. The primary key allows for quick reading, updating, or deletion of rows. The straightforward data model of Kudu facilitates the migration of legacy applications as well as the development of new ones, eliminating concerns about encoding data into binary formats or navigating through cumbersome JSON databases. Additionally, tables in Kudu are self-describing, enabling the use of standard analysis tools like SQL engines or Spark. With user-friendly APIs, Kudu ensures that developers can easily integrate and manipulate their data. This approach not only streamlines data management but also enhances overall efficiency in data processing tasks.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Flink Yes 
Apache Spark Yes 
Apache Hive Yes 
Apache Impala Yes 
Apache NiFi No 
BigBI No 
Cazpian Yes 
Cloudera Data Warehouse No 
CodeConductor Yes 
E-MapReduce No 
Google Cloud Lakehouse Yes 
Hadoop No 
Impala Yes 
Presto Yes 
R2 SQL Yes 
Stackable Yes 
StarRocks Yes 
Streamkap Yes 
Tabular Yes 
Trino Yes 

Integrations

Apache Flink Yes 
Apache Spark Yes 
Apache Hive No 
Apache Impala No 
Apache NiFi Yes 
BigBI Yes 
Cazpian No 
Cloudera Data Warehouse Yes 
CodeConductor No 
E-MapReduce Yes 
Google Cloud Lakehouse No 
Hadoop Yes 
Impala No 
Presto No 
R2 SQL No 
Stackable No 
StarRocks No 
Streamkap No 
Tabular No 
Trino No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Apache Software Foundation

Founded

1999

Country

United States

Website

iceberg.apache.org

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

kudu.apache.org/overview.html

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

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