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Data Lakes

Data Lake Processing

Data Lakes are the foundation for each organization's data landscape. They are used as sources for decision-making systems and machine learning models, so it is extremely important to ensure data lake processing is smooth and effective.
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How to address data lake processing challenges?

Data Lake processing starts with the identification of data sources. These days, data comes from a variety of sources. External APIs, streaming data, and various file formats can be examples. Each data source type has its characteristics and needs special processing. Because of that complexity, Data Lake ingestion pipelines face multiple challenges. Some of them are high data volumes, different load frequencies, and data quality. On top of that, with an increased amount of processed data, there is a constantly growing demand for resources. Highly scalable cloud environments equipped with proven open-source processing engines can be the answer for most Data Lake processing challenges. Of course, these are only foundation blocks that could be used to build effective processing pipelines.

Handle high volumes and variety of data sources

Data processing engines can handle multiple data source types and thanks to parallel processing allow for huge data volume ingestion.

Allow for dynamically changing workloads

Data ingestion starts while source data becomes available. Moreover data ingestion processing can happen simultaneously with data analytics processing. This leads to peaks in workloads which should be handled without disruption.

Processing power at hand and without over-paying

Processing power should be used wisely which means it should increase on demand and release resources when no longer used. Serverless processing can help with that.

Data Accessibility and Integration

Data lake processing enables seamless integration and availability of diverse data sources, facilitating comprehensive analysis. The approach supports various data formats, making it easy to access and combine data from multiple sources for unified analysis.

At C&F, we base our solutions on highly available and scalable cloud environments. This is our foundation for data lake processing. On top of that, we use high-performance processing engines and data analytics-capable languages to build data pipelines. Since our clients often have very complex workloads, we address that requirement by creating flexible platforms for fast new data source onboarding and easy-to-use declarative data pipeline language. We believe processing power and flexibility is insufficient for successful data lake processing; therefore, we encapsulate it within a data platform that allows for data quality verification and pipeline monitoring equipped with an advanced set of data events and notifications.

Overview

Data lakes store vast amounts of raw data that comes from disparate sources and can be structured, semi-structured, or unstructured data. This is different to a data warehouse, which transforms and processes data during ingestion. Organizations rely on data stored in data lakes for big data, advanced analytics, and machine learning. Our Data Lake Processing solutions are built on a foundation of highly available, scalable cloud environments. We then use high-performance processing engines to build data pipelines capable of handling high volumes of data from multiple sources. Our ultimate priority is seamless integration and availability of data so it can be easy for organizations to access and combine data from multiple sources for unified analysis.

Experts in the field

Dariusz Król
Senior Solutions Architect
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Michał Majkowski
Principal Software Architect
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