Seems like every major enterprise these days is undergoing a digital transformation that includes mass-migration of their data analytics workload to the Cloud.READ MORE
Business groups of all shapes and sizes have digitally transformed and are implementing a wide variety of data science and data analytics projects. Whether it’s the marketing group, sales operations group, customer service, finance, supply chain, product or any other function, these groups’ hunger for well-governed, high-quality and up-to-date data, ready-made for their analysis needs, has never been greater. Up to now, many organizations have tried to meet this need with centralized data engineering teams, but such teams can struggle to keep up with the increasing demand from all of these various functions with the timeliness that they are expecting.READ MORE
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.READ MORE
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.READ MORE
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.
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!