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What is Streamkap?

Streamkap is an innovative streaming ETL platform that leverages Apache Kafka and Flink, aiming to swiftly transition from batch ETL processes to streaming within minutes. It facilitates the transfer of data with a latency of mere seconds, utilizing change data capture to minimize disruptions to source databases while providing real-time updates. The platform boasts numerous pre-built, no-code connectors for various data sources, automatic management of schema changes, updates, normalization of data, and efficient high-performance CDC for seamless data movement with minimal impact. With the aid of streaming transformations, it enables the creation of faster, more cost-effective, and richer data pipelines, allowing for Python and SQL transformations that cater to prevalent tasks such as hashing, masking, aggregating, joining, and unnesting JSON data. Furthermore, Streamkap empowers users to effortlessly connect their data sources and transfer data to desired destinations through a reliable, automated, and scalable data movement framework, and it accommodates a wide array of event and database sources to enhance versatility. As a result, Streamkap stands out as a robust solution tailored for modern data engineering needs.

What is Google Cloud Data Fusion?

Open core technology enables the seamless integration of hybrid and multi-cloud ecosystems. Based on the open-source project CDAP, Data Fusion ensures that users can easily transport their data pipelines wherever needed. The broad compatibility of CDAP with both on-premises solutions and public cloud platforms allows users of Cloud Data Fusion to break down data silos and tap into valuable insights that were previously inaccessible. Furthermore, its effortless compatibility with Google’s premier big data tools significantly enhances user satisfaction. By utilizing Google Cloud, Data Fusion not only bolsters data security but also guarantees that data is instantly available for comprehensive analysis. Whether you are building a data lake with Cloud Storage and Dataproc, loading data into BigQuery for extensive warehousing, or preparing data for a relational database like Cloud Spanner, the integration capabilities of Cloud Data Fusion enable fast and effective development while supporting rapid iterations. This all-encompassing strategy ultimately empowers organizations to unlock greater potential from their data resources, fostering innovation and informed decision-making. In an increasingly data-driven world, leveraging such technologies is crucial for maintaining a competitive edge.

Media

Media

Integrations Supported

Google Cloud BigQuery
AWS PrivateLink
Amazon DocumentDB
Apache Kafka
Azure Data Lake
CockroachDB
Databricks
Elasticsearch
Google Cloud Datastream
IntelliPay
JSON
MariaDB
OpenSearch
Oracle Database
Pantomath
PostgreSQL
Python
SQL Server
Vitess

Integrations Supported

Google Cloud BigQuery
AWS PrivateLink
Amazon DocumentDB
Apache Kafka
Azure Data Lake
CockroachDB
Databricks
Elasticsearch
Google Cloud Datastream
IntelliPay
JSON
MariaDB
OpenSearch
Oracle Database
Pantomath
PostgreSQL
Python
SQL Server
Vitess

API Availability

Has API

API Availability

Has API

Pricing Information

$600 per month
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

Streamkap

Date Founded

2022

Company Location

United States

Company Website

streamkap.com

Company Facts

Organization Name

Google

Company Location

United States

Company Website

cloud.google.com/data-fusion

Categories and Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

Categories and Features

ETL

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

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