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What is Stackable?

The Stackable data platform was designed with an emphasis on adaptability and transparency. It features a thoughtfully curated selection of premier open-source data applications such as Apache Kafka, Apache Druid, Trino, and Apache Spark. In contrast to many of its rivals that either push their proprietary offerings or increase reliance on specific vendors, Stackable adopts a more forward-thinking approach. Each data application seamlessly integrates and can be swiftly added or removed, providing users with exceptional flexibility. Built on Kubernetes, it functions effectively in various settings, whether on-premises or within cloud environments. Getting started with your first Stackable data platform requires only stackablectl and a Kubernetes cluster, allowing you to begin your data journey in just minutes. You can easily configure your one-line startup command right here. Similar to kubectl, stackablectl is specifically designed for effortless interaction with the Stackable Data Platform. This command line tool is invaluable for deploying and managing stackable data applications within Kubernetes. With stackablectl, users can efficiently create, delete, and update various components, ensuring a streamlined operational experience tailored to your data management requirements. The combination of versatility, convenience, and user-friendliness makes it a top-tier choice for both developers and data engineers. Additionally, its capability to adapt to evolving data needs further enhances its appeal in a fast-paced technological landscape.

What is MLlib?

MLlib, the machine learning component of Apache Spark, is crafted for exceptional scalability and seamlessly integrates with Spark's diverse APIs, supporting programming languages such as Java, Scala, Python, and R. It boasts a comprehensive array of algorithms and utilities that cover various tasks including classification, regression, clustering, collaborative filtering, and the construction of machine learning pipelines. By leveraging Spark's iterative computation capabilities, MLlib can deliver performance enhancements that surpass traditional MapReduce techniques by up to 100 times. Additionally, it is designed to operate across multiple environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud settings, while also providing access to various data sources like HDFS, HBase, and local files. This adaptability not only boosts its practical application but also positions MLlib as a formidable tool for conducting scalable and efficient machine learning tasks within the Apache Spark ecosystem. The combination of its speed, versatility, and extensive feature set makes MLlib an indispensable asset for data scientists and engineers striving for excellence in their projects. With its robust capabilities, MLlib continues to evolve, reinforcing its significance in the rapidly advancing field of machine learning.

Media

Media

Integrations Supported

Apache HBase
Apache Hive
Apache Spark
Kubernetes
Amazon EC2
Apache Airflow
Apache Cassandra
Apache Iceberg
Apache Kafka
Apache ZooKeeper
Docker
Git
Hadoop
Java
MapReduce
MinIO
OpenSearch
Prometheus
R
Scala

Integrations Supported

Apache HBase
Apache Hive
Apache Spark
Kubernetes
Amazon EC2
Apache Airflow
Apache Cassandra
Apache Iceberg
Apache Kafka
Apache ZooKeeper
Docker
Git
Hadoop
Java
MapReduce
MinIO
OpenSearch
Prometheus
R
Scala

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

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

Stackable

Date Founded

2020

Company Location

Germany

Company Website

stackable.tech/

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1995

Company Location

United States

Company Website

spark.apache.org/mllib/

Categories and Features

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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