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Average Ratings 0 Ratings
Description
IBM Analytics for Apache Spark offers a versatile and cohesive Spark service that enables data scientists to tackle ambitious and complex inquiries while accelerating the achievement of business outcomes. This user-friendly, continually available managed service comes without long-term commitments or risks, allowing for immediate exploration. Enjoy the advantages of Apache Spark without vendor lock-in, supported by IBM's dedication to open-source technologies and extensive enterprise experience. With integrated Notebooks serving as a connector, the process of coding and analytics becomes more efficient, enabling you to focus more on delivering results and fostering innovation. Additionally, this managed Apache Spark service provides straightforward access to powerful machine learning libraries, alleviating the challenges, time investment, and risks traditionally associated with independently managing a Spark cluster. As a result, teams can prioritize their analytical goals and enhance their productivity significantly.
Description
The Stackable data platform was crafted with a focus on flexibility and openness. It offers a carefully selected range of top-notch open source data applications, including Apache Kafka, Apache Druid, Trino, and Apache Spark. Unlike many competitors that either promote their proprietary solutions or enhance vendor dependence, Stackable embraces a more innovative strategy. All data applications are designed to integrate effortlessly and can be added or removed with remarkable speed. Built on Kubernetes, it is capable of operating in any environment, whether on-premises or in the cloud. To initiate your first Stackable data platform, all you require is stackablectl along with a Kubernetes cluster. In just a few minutes, you will be poised to begin working with your data. You can set up your one-line startup command right here. Much like kubectl, stackablectl is tailored for seamless interaction with the Stackable Data Platform. Utilize this command line tool for deploying and managing stackable data applications on Kubernetes. With stackablectl, you have the ability to create, delete, and update components efficiently, ensuring a smooth operational experience for your data management needs. The versatility and ease of use make it an excellent choice for developers and data engineers alike.
API Access
Has API
API Access
Has API
Integrations
Apache Spark
Apache Airflow
Apache Druid
Apache HBase
Apache Hive
Apache Iceberg
Apache Kafka
Apache NiFi
Apache ZooKeeper
Docker
Integrations
Apache Spark
Apache Airflow
Apache Druid
Apache HBase
Apache Hive
Apache Iceberg
Apache Kafka
Apache NiFi
Apache ZooKeeper
Docker
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
www.ibm.com/analytics/ca/en/technology/cloud-data-services/spark-as-a-service/
Vendor Details
Company Name
Stackable
Founded
2020
Country
Germany
Website
stackable.tech/
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Data Science
Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports
Integration
Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services
Product 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