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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 Dataflow?

A data processing solution that combines both streaming and batch functionalities in a serverless, cost-effective manner is now available. This service provides comprehensive management for data operations, facilitating smooth automation in the setup and management of necessary resources. With the ability to scale horizontally, the system can adapt worker resources in real time, boosting overall efficiency. The advancement of this technology is largely supported by the contributions of the open-source community, especially through the Apache Beam SDK, which ensures reliable processing with exactly-once guarantees. Dataflow significantly speeds up the creation of streaming data pipelines, greatly decreasing latency associated with data handling. By embracing a serverless architecture, development teams can concentrate more on coding rather than navigating the complexities involved in server cluster management, which alleviates the typical operational challenges faced in data engineering. This automatic resource management not only helps in reducing latency but also enhances resource utilization, allowing teams to maximize their operational effectiveness. In addition, the framework fosters an environment conducive to collaboration, empowering developers to create powerful applications while remaining free from the distractions of managing the underlying infrastructure. As a result, teams can achieve higher productivity and innovation in their data processing initiatives.

Media

Media

Integrations Supported

Azure Data Lake
Databricks
Google Cloud BigQuery
Google Cloud SQL
IBM Db2
JSON
MotherDuck
MySQL
Oracle Database
Python
Slack
Terraform
Vitess

Integrations Supported

Google Cloud Confidential VMs
Google Cloud Datastream
Google Cloud Managed Service for Apache Airflow
Orchestra
Pantomath
Sedai
Ternary

API Availability

Has API

API Availability

Pricing Information

$600 per month
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Streamkap

Date Founded

2022

Company Location

United States

Company Website

streamkap.com

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/dataflow

Categories and Features

Data Pipeline

Not specified

ETL

Not specified

Categories and Features

Data Engineering

Not specified

Data Pipeline

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

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