Data Engineering Podcast
JULY 14, 2019
Summary Successful machine learning and artificial intelligence projects require large volumes of data that is properly labelled. The challenge is that most data is not clean and well annotated, requiring a scalable data labeling process. Ideally this process can be done using the tools and systems that already power your analytics, rather than sending data into a black box.
Confluent
JULY 16, 2019
Have you ever realized that, according to the latest FBI report , more than 80% of all crimes are property crimes, such as burglaries? And that the FBI clearance figures indicate that only 13% of all burglaries in 2017 were cleared due to lack of witnesses and/or physical evidence? How cool would it be to build your own burglar alarm system that can alert you before the actual event takes place simply by using a few network-connected cameras and analyzing the camera images with Apache Kafka ® ,
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Teradata
JULY 16, 2019
Teradata Vantage's NewSQL Engine's performance-enhancing options include column-row hybrid partitioning. Find out how to take advantage of this great feature.
Cloudera
JULY 15, 2019
At a time when machine learning, deep learning, and artificial intelligence capture an outsize share of media attention, jobs requiring SQL skills continue to vastly outnumber jobs requiring those more advanced skills. Influential data scientists often point to SQL as the most important yet underrated skill for anyone who works with data. SQL is today—and will remain for the foreseeable future—a vital foundational skill for a wide range of data professionals working in different roles across dif
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
Cloud Academy
JULY 16, 2019
Designing a streaming data pipeline presents many challenges, particularly around specific technology requirements. When designing a cloud-based solution, an architect is no longer faced with the question, “How do I get this job done with the technology we have?” but rather, “What is the right technology to support my use case?” In this blog post, we will walk through some initial scoping steps and walk through an example.
Zalando Engineering
JULY 14, 2019
Project Highlights Kopf - Kubernetes Operator Pythonic Framework now supports built-in resources and can be used to write controllers of any kind (pods, namespaces, mixed), not only of custom resources. Check out the latest release for more details [link] Skipper publishes new releases weekly. Some of the important features were implemented such as support to proxy Kubernetes API server and support Kubernetes externalName services from ingress.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
Cloudera
JULY 16, 2019
A combination of on-premises, public, and private cloud platforms and data centers describes the reality for today’s businesses. For some, this hybrid mix was born of rigorous planning or even a “cloud-first” mandate. For others, however, it evolved organically. Businesses merged, data centers ran out of room to expand, and departments made independent choices or engaged in shadow IT.
Rockset
JULY 18, 2019
Rockset is a schemaless SQL data platform. It is designed to support SQL on raw data. While most SQL databases are strongly and statically typed, data within Rockset is strongly but dynamically typed. Dynamic typing makes it difficult for us to adopt off-the-shelf SQL query optimizers since they are designed for statically typed data where the types of the columns are known ahead of time.
Confluent
JULY 17, 2019
Together, MongoDB and Apache Kafka ® make up the heart of many modern data architectures today. Integrating Kafka with external systems like MongoDB is best done though the use of Kafka Connect. This API enables users to leverage ready-to-use components that can stream data from external systems into Kafka topics, as well as stream data from Kafka topics into external systems.
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