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Data Lakes

Data Lake Orchestration

Combining data from multiple sources and different file formats at different speeds is a real challenge. Orchestrating data lake ingestion pipelines helps data teams work efficiently when new data sets need to be ingested.
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How to implement Data Lake Orchestration?

We often see organizations struggling with massive amounts of data from disparate sources. Effectively handling this data requires robust orchestration solutions that can streamline the entire data workflow. C&F offers comprehensive data lake orchestration solutions designed to automate and optimize every aspect of the data pipeline. From data ingestion to transformation, processing and analysis, our platform solutions make it easy for organizations to maximize the value of their data assets. We start by ingesting data from a variety of sources, including databases, streaming platforms, cloud storage, and more, while ensuring that the data lake is continuously updated with the latest information. Our solutions leverage cloud-native technologies to provide elastic scalability, and by monitoring and intelligently consuming resources, we deliver the best performance at a predictable price.

Faster data onboarding

Data lake ingestion pipelines defined in a simple and declarative language enable data teams to quickly adapt to change.

Flexible workloads

High scalability and concurrency with cloud-based, containerized solutions.

Data observability

A unified data lake orchestration platform enables better control of data loads and rapid identification of data problem sources.

Improved error handling and notifications

Data lake orchestration pipelines come with predefined mechanisms for error handling and notification events that are sent through different communication channels depending on the data issue.

A solid data lake orchestration solution should provide a unified platform that can handle multiple data formats. In addition to traditional file formats and databases, today's data sources include data streams and API-consumed data. The need to handle different types of data at different speeds dictates the need for flexible solutions that can easily scale for different workloads. Using lightweight microservices based on industry-standard frameworks running in containerized environments in the cloud is a good foundation for data lake orchestration platforms. When building data lake orchestration platforms, it is equally important to provide an easy-to-use pipeline definition framework coupled with a user-friendly interface. This allows for faster ingestion of new data sets and monitoring of data processing.

Overview

Data Lake Orchestration streamlines data workflows to ensure data integrity and optimized data processing. With organizations managing vast amounts of data from numerous different sources, data orchestration is essential to efficiently manage data processes within a data lake. We use advanced data orchestration tools to automate various data workflows, such as data integration, data processing, and management, and ensure they’re executed in a timely manner. When building data orchestration platforms, we focus on providing easy-to-use data pipelines and a user-friendly interface for faster ingestion and monitoring. The result is improved data quality that can be leveraged for accurate insights, analytics, and decision-making.

Experts in the field

Michał Majkowski
Principal Software Architect
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