Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Amazon Managed Streaming for Apache Kafka (Amazon MSK) simplifies the process of creating and operating applications that leverage Apache Kafka for handling streaming data. As an open-source framework, Apache Kafka enables the construction of real-time data pipelines and applications. Utilizing Amazon MSK allows you to harness the native APIs of Apache Kafka for various tasks, such as populating data lakes, facilitating data exchange between databases, and fueling machine learning and analytical solutions. However, managing Apache Kafka clusters independently can be quite complex, requiring tasks like server provisioning, manual configuration, and handling server failures. Additionally, you must orchestrate updates and patches, design the cluster to ensure high availability, secure and durably store data, establish monitoring systems, and strategically plan for scaling to accommodate fluctuating workloads. By utilizing Amazon MSK, you can alleviate many of these burdens and focus more on developing your applications rather than managing the underlying infrastructure.
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, OpenSearch, 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
No
API Access
Has API
No
Integrations
Apache Kafka
Yes
5X
Yes
Apache Airflow
No
Apache Druid
No
Apache HBase
No
Apache Hive
No
Apache Iceberg
No
Apache NiFi
No
Argonaut
Yes
Datadog
Yes
Integrations
Apache Kafka
Yes
5X
No
Apache Airflow
Yes
Apache Druid
Yes
Apache HBase
Yes
Apache Hive
Yes
Apache Iceberg
Yes
Apache NiFi
Yes
Argonaut
No
Datadog
No
Pricing Details
$0.0543 per hour
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
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
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/msk/
Vendor Details
Company Name
Stackable
Founded
2020
Country
Germany
Website
stackable.tech/
Product Features
Streaming Analytics
Data Enrichment
No
Data Wrangling / Data Prep
No
Multiple Data Source Support
No
Process Automation
No
Real-time Analysis / Reporting
No
Visualization Dashboards
No
Product Features
Data Management
Customer Data
No
Data Analysis
No
Data Capture
No
Data Integration
No
Data Migration
Yes
Data Quality Control
No
Data Security
Yes
Information Governance
No
Master Data Management
No
Match & Merge
No
Data Warehouse
Ad hoc Query
No
Analytics
Yes
Data Integration
Yes
Data Migration
Yes
Data Quality Control
No
ETL - Extract / Transfer / Load
Yes
In-Memory Processing
No
Match & Merge
No