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No-code business intelligence service y42 raises $2.9M seed round

TechCrunch

So out of that frustration, I decided to develop an internal tool that was actually quite usable and in 2016, I decided to turn it into an actual company. . “I was using tools like Tableau and Alteryx, and it was really hard to glue them together — and they were quite expensive.

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Forget the Rules, Listen to the Data

Hu's Place - HitachiVantara

DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective. A 2016 CyberSource report claimed that over 90% of online fraud detection platforms use transaction rules to detect suspicious transactions which are then directed to a human for review.

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DataOps: Adjusting DevOps for Analytics Product Development

Altexsoft

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

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Hortonworks New Distribution Strategy and New Streaming Analytics

CTOvision

HDF is a data-in-motion platform for real-time streaming of data and is a cornerstone technology for the Internet of Anything to ingest data from any source to any destination. now integrates streaming analytics engines Apache Kafka and Apache Storm for delivering actionable intelligence. Related articles.

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AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

d2iq

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

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Five Trends for 2019

Hu's Place - HitachiVantara

In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data.

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How Our Paths Brought Us to Data and Netflix

Netflix Tech

I bring my breadth of big data tools and technologies while Julie has been building statistical models for the past decade. A lot of my learning and training was self-guided until 2016, when a manager at my last company took a chance on me and helped me make the rare transfer from a role in HR to Data Science.

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