CIO
MARCH 12, 2024
The key to success in the software development lifecycle is the quality assurance (QA) and verification process, Ramakrishnan says. Additionally, we are looking into training LLMs [large language models] on our code base to unlock further productivity boosts for our developers and data engineers.
CIO
JANUARY 6, 2023
But 86% of technology managers also said that it’s challenging to find skilled professionals in software and applications development, technology process automation, and cloud architecture and operations. Companies will have to be more competitive than ever to land the right talent in these high-demand areas.
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Altexsoft
AUGUST 29, 2023
Data lakes emerged as expansive reservoirs where raw data in its most natural state could commingle freely, offering unprecedented flexibility and scalability. This article explains what a data lake is, its architecture, and diverse use cases. Watch our video explaining how data engineering works.
AWS Machine Learning - AI
JANUARY 26, 2024
Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications.
Altexsoft
SEPTEMBER 9, 2022
Today, such modern data management frameworks as DataOps strongly rely on effective metadata capture and management to bring order into the chaotic data flows. Plus, a data fabric architecture design approach is also based on metadata as one of the main building blocks. Metadata quality assurance.
Abhishek Tiwari
JULY 12, 2018
Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.
Altexsoft
JULY 19, 2022
In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two. What data mesh IS.
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