Systems | Development | Analytics | API | Testing

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Speed Up Development With Powered by Fivetran

Powered by Fivetran (PBF) provides a simple framework for developers to go beyond internal analytics projects to build data pipelines into their applications within the Fivetran platform. With no engineering overhead, you can easily access hundreds of customer accounts across countless Fivetran-supported data sources, including advertising platforms, CRM systems, databases, web events and more.

A perfect environment to learn & develop on Apache Kafka

Apache Kafka has gained traction as one of the most widely adopted technologies for building streaming applications - but introducing it (and scaling it) into your business can be a struggle. The problem isn’t with Kafka itself so much as the different components you need to learn and different tools required to operate it. For those motivated enough, you can invest money, effort and long Friday nights into learning, fixing and streamlining Kafka - and you’ll get there.

Creating value from legacy data - The whys and the hows of legacy system integration

While there are many challenges young companies might struggle with, they certainly escaped one that is a blessing and a curse at the same time – the legacy IT systems. Data is indeed one of the companies’ most valued assets, as knowledge (read, ‘data’) empowers better, more informed business decisions. Moreover, chances are your organization already has most of the knowledge it needs. The only caveat, though, is that it might be inaccessible and therefore, pretty useless.

The Rise Of Connected Manufacturing And How Data Is Driving Innovation, Part I

This interview was conducted by Cindy Maike, VP Industry Solutions The shift towards Industry 4.0 is improving manufacturing efficiency and the factory of the future will increasingly be driven by technology like the Internet of Things (IoT), Automation, Artificial Intelligence (AI), and Cloud Computing.

MLRun Functions DEMO: Python Jupyter (Open-Source Data Science Orchestration + Experiment Tracking)

MLRun is a generic and convenient mechanism for #data scientists and software developers to build, run, and monitor #machinelearning (ML) tasks and pipelines on a scalable cluster while automatically tracking executed code, metadata, inputs, and outputs. On-Premise or Barebone/Metal - including Edge AI / Analytics Customers include NetApp, Quadient, Payoneer (and many more).

How to handle errors on elastic.io

You can see if any of your flows have errors as soon as you log in to your dashboard. It shows you the number of records processed in total – in green – and the number of records with errors – in red. You can also see the same information if you click on the corresponding flow from the dashboard. In my case, you can see I have three records processed and all three returned errors.