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Data Lakes

Data Lake Storage

Data lakes are essential in data storage, providing scalable, flexible, cost-effective solutions. They offer a centralized repository for different data types, improve data integration, and enhance data management with advanced metadata and cataloging features.
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What are the considerations to store massive amounts of data in data lake?

As C&F, we experience processes where data comes in at different speeds and can contain various levels of structuring. Therefore, we use different storage formats depending on the source data type, further processing, and analytical needs. Columnar storage formats allow us to compromise query performance, scalability, and data storage needs. We use modern file formats such as Parquet, AVRO, IceBerg, and DeltaLake, as they allow flexibility in schema changes and offer high performance for analytical processing. Combined with cloud storage and appropriate retention policies, they are good candidates for us for the data lake storage layer.

High query performance

Support analytical workloads through file formats that enable flexible and fast querying.

Adopt to changing schemas

Make changes to file schemas in a way that does not result in a disruption to the data pipeline.

Allow for data updates

Update data in the data lake efficiently and possibly without rewriting files.

Centralized data repository

Build a unified repository, breaking down data silos and facilitating end-to-end data storage and access. Support end-to-end analytics and decision-making by creating a single source of truth.

When designing data lake ingestion pipelines, it is essential to consider underlying data storage. There are multiple data formats and storage platforms available. It is necessary to consider the amount and frequency of data ingested and analytical needs. Today’s cloud platforms offer almost unlimited storage capacity; however, this comes at a cost. Good data storage design should be a compromise between high performance, flexibility for schema changes, and storage costs. At C&F, we consider all these factors and develop a storage strategy that addresses analytical needs, regulatory requirements, and project budget restrictions.

Overview

Data lakes are repositories used to store vast amounts of raw data that can be accessed for big data analytics, insights, and decision-making. Unlike data warehouses, which transform data before storing it, data lakes can store structured, semi-structured, or unstructured data in any format. Our Data Lake Storage services focus on building customized solutions capable of storing massive amounts of data. With a unified, single source of truth, your organization can break down data silos and facilitate ent-to-end data storage. We build our custom solutions to balance high performance, flexibility for schema changes, and storage costs to offer you solutions that meet your analytical needs, budget, and regulatory requirements.

Experts in the field

Michał Majkowski
Principal Software Architect
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