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Ascend.io lands $31M to automate data pipeline orchestration

TechCrunch

The company’s platform is designed to give data teams a unified platform to automate the orchestration of data engineering and analytics workloads, he says, ideally reducing the need for manual configuration. Rather, it was the ability to scale the productivity of the people who work with data.

Data 230
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Core technologies and tools for AI, big data, and cloud computing

O'Reilly Media - Ideas

There are many articles that point to the explosion of data, but in order for that data that be useful for analytics and ML, it has to be collected, transported, cleaned, stored, and combined with other data sources. AI and Data technologies in the cloud. Building a Serverless Big Data Application on AWS”.

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Apiumhub among top IT industry leaders in Code Europe event

Apiumhub

His current technical expertise focuses on integration platform implementations, Azure DevOps, and Cloud Solution Architectures. Steef-Jan is a board member of the Dutch Azure User Group, a regular speaker at conferences and user groups, and he writes for InfoQ, and Serverless Notes. Twitter: [link] Linkedin: [link].

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Altexsoft - Untitled Article

Altexsoft

We’ll review all the important aspects of their architecture, deployment, and performance so you can make an informed decision. Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. Data warehouse architecture.

Backup 115
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The Good and the Bad of Snowflake Data Warehouse

Altexsoft

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. Data warehousing in a nutshell. Snowflake architecture overview.

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The Good and the Bad of Apache Spark Big Data Processing

Altexsoft

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. How data engineering works in a nutshell.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

ERP engineering squad - supply chain planning, purchase order management, product lifecycle management, merchandise planning, etc. Back-office engineering squad - customer support, business intelligence, real-estate management, systems for finance & HR, etc. You want to move fast. Remember code ownership is overrated.