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Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.
This conversation was useful for getting a better idea of the challenges that exist in large scale data analytics, and the current state of the tradeoffs between data lakes and datawarehouses in the cloud. Support the show and get your data projects in order!
You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data?
Users today are asking ever more from their datawarehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?
The platform approach to enable the citizen machine learning engineers is a great perspective while building both the Data & ML platform. Architectural patterns like LambdaArchitecture and Kappa Architecture emerged to bridge the gap between real-time and batch data processing.
Data ingestion is the process of collecting data from various sources and moving it to your datawarehouse or lake for processing and analysis. It is the first step in modern data management workflows. Data ingestion is the process of acquiring and importing data for use, either immediately or in the future.
Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.
As per Apache, “ Apache Spark is a unified analytics engine for large-scale data processing ” Spark is a cluster computing framework, somewhat similar to MapReduce but has a lot more capabilities, features, speed and provides APIs for developers in many languages like Scala, Python, Java and R.
There are many uses and benefits for real-time traffic simulation and prediction projects using big data. This project is a LambdaArchitecture program that tracks Chicago's streets' traffic conditions, including congestion and safety. Simulating real-time traffic has successfully been modeled.
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