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What is SAS Data Loader for Hadoop?

Easily import or retrieve your data from Hadoop and data lakes, ensuring it's ready for report generation, visualizations, or in-depth analytics—all within the data lakes framework. This efficient method enables you to organize, transform, and access data housed in Hadoop or data lakes through a straightforward web interface, significantly reducing the necessity for extensive training. Specifically crafted for managing big data within Hadoop and data lakes, this solution stands apart from traditional IT tools. It facilitates the bundling of multiple commands to be executed either simultaneously or in a sequence, boosting overall workflow efficiency. Moreover, you can automate and schedule these commands using the public API provided, enhancing operational capabilities. The platform also fosters collaboration and security by allowing the sharing of commands among users. Additionally, these commands can be executed from SAS Data Integration Studio, effectively connecting technical and non-technical users. Not only does it include built-in commands for various functions like casing, gender and pattern analysis, field extraction, match-merge, and cluster-survive processes, but it also ensures optimal performance by executing profiling tasks in parallel on the Hadoop cluster, which enables the smooth management of large datasets. This all-encompassing solution significantly changes your data interaction experience, rendering it more user-friendly and manageable than ever before, while also offering insights that can drive better decision-making.

What is Apache Parquet?

Parquet was created to offer the advantages of efficient and compressed columnar data formats across all initiatives within the Hadoop ecosystem. It takes into account complex nested data structures and utilizes the record shredding and assembly method described in the Dremel paper, which we consider to be a superior approach compared to just flattening nested namespaces. This format is specifically designed for maximum compression and encoding efficiency, with numerous projects demonstrating the substantial performance gains that can result from the effective use of these strategies. Parquet allows users to specify compression methods at the individual column level and is built to accommodate new encoding technologies as they arise and become accessible. Additionally, Parquet is crafted for widespread applicability, welcoming a broad spectrum of data processing frameworks within the Hadoop ecosystem without showing bias toward any particular one. By fostering interoperability and versatility, Parquet seeks to enable all users to fully harness its capabilities, enhancing their data processing tasks in various contexts. Ultimately, this commitment to inclusivity ensures that Parquet remains a valuable asset for a multitude of data-centric applications.

Media

Media

Integrations Supported

Hadoop
Apache DataFusion
Arroyo
Autymate
Data Sentinel
Ficstar
Gable
Gravity Data
IBM Db2 Event Store
Mage Platform
Meltano
Microsoft Power BI
OrcaSheets
SAS Business Rules Manager
SAS Risk Management
SSIS Integration Toolkit
Semarchy xDI
Timbr.ai
Tonic Ephemeral
e6data

Integrations Supported

Hadoop
Apache DataFusion
Arroyo
Autymate
Data Sentinel
Ficstar
Gable
Gravity Data
IBM Db2 Event Store
Mage Platform
Meltano
Microsoft Power BI
OrcaSheets
SAS Business Rules Manager
SAS Risk Management
SSIS Integration Toolkit
Semarchy xDI
Timbr.ai
Tonic Ephemeral
e6data

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

SAS

Date Founded

1976

Company Location

United States

Company Website

www.sas.com/en_us/software/data-loader-for-hadoop.html

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

parquet.apache.org

Categories and Features

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Categories and Features

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