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Data Lakes

Massive Data Ingestion

Data acquisition platforms are crucial in capturing vast amounts of data from multiple sources at different speeds and formats. They need to handle this variety of data efficiently by scaling to process multiple workloads.
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How to design efficient data ingestion process?

Massive data ingestion aims to collect and import large volumes of data from various sources into data lakes or processing systems for further analysis and utilization. At C&F, we specialize in data ingestion across many different sources, among other databases (both SQL and NO-SQL), logs, device sensors, IoT devices, external system APIs, and multiple file formats. Given this experience, we have developed a list of building blocks essential when building data ingestion pipelines for our clients. Cloud services, open-source frameworks, and data science-supporting language services running in containerized scalable environments are the foundation for solutions that provide our clients with the best price/performance ratio. Next to data processing performance, our data engineering team applies validation, transformation, and catalog solutions so that ingested data becomes an asset ready to derive value from.

Flexible and scalable processing

Take advantage of cloud-based, containerized environments that can be easily scaled to support multiple workloads. Control resource utilizaton at multiple levels and allocate to specific business units.

Good price-performance ratio

Optimize processing with cloud solutions toenable data ingestion and adapt to changing processingrequirements while respecting resource usage limits.

Data observability benefits

Implement data observability to identify, troubleshoot and resolve data issues in near real-time. Achieve an understanding of data health and condition across the data ecosystem.

Support for multiple analytics use cases

Use column-based file formats to enable a wide range of data analytics applications. Ensure fast query performance and efficient data retrieval, making it ideal for data warehousing, business analytics, analytics, machine learning, IoT processing and ad hoc queries.

In my experience, managing massive data capture requires a robust infrastructure and strategic alignment with changing deployment trends and technology standards. Using data streaming, cloud-native architectures, and scalable computing engines enables our clients to collect massive amounts of data and generate actionable insights. In my opinion massive data ingestionrequires efficient data collection based on well-designed orchestration of data flows, appropriate monitoring ,operational tools and procedures.

Overview

A data ingestion framework involves collecting incoming data from numerous sources and importing it into a centralized location, such as data lakes or data warehouses. Once data is stored in a data lake or data warehouse, it can be processed or used for analytics. Every organization needs to ingest data before it can be used by data engineers or data teams for business intelligence, artificial intelligence (AI), or machine learning. A typical data pipeline uses various data ingestion tools to transform raw data into consistent formats ready for analysis, helping improve data quality and consistency. Our solutions can help your organization build a robust data ingestion pipeline that’s flexible, scalable, and supports multiple analytics use cases.

Experts in the field

Michał Majkowski
Principal Software Architect
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