CATEGORY: Data Engineering

Why ingest when you can Onboard Data?

WRITTEN BY Amar Arsikere June 17, 2020
Big Data

Every data analytics project starts with the critical first step of creating and operationalizing healthy data lakes. A unified data lake is created by onboarding multiple data sources. Onboarding a data source is more than ingesting the data once.

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The Top 5 Data Preparation Challenges to Get Big Data Pipelines to Run in Production

WRITTEN BY Ramesh Menon February 12, 2019
Data Engineering

After data is ingested into a data lake, data engineers need to transform this data in preparation for downstream use. Challenges in data preparation tend to be a broad collection of issues that add up over time to create significant ongoing maintenance and management issues.

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With a strong focus on data engineering automation, the Infoworks blog includes a specific category for data engineering articles. In order for IT and analytics teams to extract the most value from a plethora of structured and unstructured data, organizations rely on the skills and expertise of some to design, build, and maintain both data warehouses and data pipelines.

With roots in both business intelligence and software engineering, data engineering represents a set of skills and knowledge necessary to collect and validate data as well as creating the mechanisms for the real-world application of how to use that data.

While some can possess various skills under the umbrella of data engineering, the individuals who specialize in it and are often referred to as data engineers. Data engineers deal less in the analysis of big data and focus more on the practical flow and access of information. Essentially, a data engineer’s primary purpose is to take raw data and transform it so that this data can be queried later on. Data science relies upon these data warehouses to keep costs down and allow for scalability.

One of the primary goals of a data engineer is to optimize the performance of their company’s big data ecosystem. With a stronger connection to the realm of software engineering, data engineering experts are often proficient in system architecture, programming, database/interface design, and sensor configuration.

Some of the most common things data engineers must be familiar with are technologies intended for data storage and manipulation such as Hadoop, NoSQL, Hive, Spark, and MapReduce.

Looking for Further Reading on Automated Data Engineering & Big Data? 

The Infoworks blog is the best place to discover helpful resources and articles which dive into best practices and unique insights from around the data engineering industry. Our blog also dives into big data news,  data ingestion best practices, data operations articles, new announcements from the team at Infoworks, and data lake news articles. Stay up to date with our blog by subscribing to our email newsletter.  

If you would like to learn more about enterprise data operations and orchestration, be sure to check out the Infoworks DataFoundry!