How AWS simplifies cloud data engineering

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cloud data engineering - AWS

Cloud data engineering is essential for organizations that are looking to use their data to gain deeper insights for better business decision making. Data engineers are tasked with storing, moving, transforming and structuring data for analytics and reporting purposes. AWS data engineers manage various AWS services to provide an integrated package that is designed to accelerate an organization’s data journey.  This blog covers a range of AWS data services that simplify cloud data engineering to reduce time and costs for businesses.

AWS Data Pipeline

A data pipeline comprises a suite of tools and processes that are applied to automate the movement and transformation of data from a source system to a target repository. In many cases, the source system will use different ways to process and store data than the target system. AWS Data Pipeline is designed to simplify and automate the movement and transformation of data. This helps data engineers to reliably process and move data between different AWS compute and storage services, as well as on-premise data sources. By using AWS Data Pipeline, organizations can quickly gain access to their data wherever it is stored. They can then transform and process the data at scale, and efficiently transfer the results to other AWS services.

AWS Data Warehouse

Cloud Data Engineering - AWS   A data warehouse is a central repository of information that can be analyzed to make better-informed business decisions. Data flows into a data warehouse from various sources (such as transactional systems and relational databases). Then data engineers and other stakeholders can access the data through business intelligence (BI) and analytics tools. AWS offers a broad set of managed services that integrate seamlessly with each other so that data engineers can quickly deploy an end-to-end analytics and data warehousing solution. For example, Amazon Redshift is a fast, fully managed, and cost-effective data warehouse service that provides petabyte-scale data warehousing and exabyte-scale data lake analytics together in one service. A key financial benefit is that businesses only pay for the warehousing they are using at any given time.

AWS Data Lake

Cloud Data Engineering - AWS   Data lakes are vast repositories of raw data that are centralized and secure. The data they house can be stored and analyzed to guide business decisions through deeper insights. Normally, setting up data lakes involves a large amount of manual work, which is often complicated and time-consuming. AWS Lake Formation simplifies the process of setting up secure data lakes, usually accelerating the process from months to weeks. The whole procedure is reduced to simply defining data sources and the data access and security policies that an organization requires.

AWS Data Analytics

Cloud Data Engineering - AWS   AWS Data Analytics is a comprehensive suite of services to fulfill all data analytics requirements and enable businesses of all sizes and industries to reinvent their business with data.  From big data analytics, log analytics, streaming analytics, and machine learning (ML) to anything in between, AWS offers purpose-built services that simplify cloud data engineering while providing the greatest levels of scalability and reducing costs for organizations. Here are a few examples of the many AWS data services available: With ProCogia acting as your AWS partner, you’ll always be one step ahead.  

Amazon Athena

This interactive query service simplifies the process of analyzing data. As well as being very easy to use, Athena is serverless, meaning there’s no infrastructure to manage so businesses only pay for the queries they run.  

Amazon CloudSearch

A managed service in the AWS cloud that makes it simple and cost-effective to set up, manage, and scale a search solution for a website or application.  

Amazon EMR

Amazon EMR makes it easy for data engineers to set up, operate, and scale big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters.  

Amazon FinSpace

Purpose-built for the Financial Services Industry (FSI), FinSpace reduces the time institutions spend finding and preparing petabytes of financial data to be ready for analysis in minutes rather than months.  

Amazon Kinesis

Makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. 

Speak to our trusted AWS data engineers

Cloud Data Engineering - AWS   ProCogia is a data science company with a wealth of AWS cloud data engineering expertise. Find out how our team of AWS engineers can simplify and accelerate your journey to becoming data-driven.


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