Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

MARCH 7, 2024

Why prompt engineering isn’t all that and a bag of SQL queries Understand vector databases Create AI differentiation with RAG Find and solve real business problems High-quality data always lives up to the hype What is prompt engineering? As a data leader, you’re going to need a lot more to drive business value with AI.

Engineering 59

Engineering High Quality Data Database Architecture 59

Precisely

JANUARY 11, 2023

To remain competitive, you must proactively and systematically pursue new ways to leverage data to your advantage. As the value of data reaches new highs, the fundamental rules that govern data-driven decision-making haven’t changed. To make good decisions, you need high-quality data.

Data Governance 52

Data Governance Government Metadata Data 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

FEBRUARY 20, 2024

There’s the dichotomy between batch and streaming, between all-in-one platforms and niche connectors in your modern data stack, and the trade-off between ease-of-use and powerful customization options. One thing’s for certain: you definitely don’t want to be writing pipelines in Python anymore.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

Knowledge Hut

JUNE 28, 2023

By examining these factors, organizations can make informed decisions on which approach best suits their data analysis and decision-making needs. Parameter Data Mining Business Intelligence (BI) Definition The process of uncovering patterns, relationships, and insights from extensive datasets.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

Monte Carlo

JANUARY 6, 2023

A data-informed product strategy 2. A data-informed product strategy Product teams are tasked with being data-informed at all stages in the product life cycle, from idea generation and product definition, to validating prototypes and building a commercially successful product. Experimentation 4. Cohort analysis 5.

Building 52

Building Project BI Data 52

AltexSoft

AUGUST 22, 2022

So, instead of replacing or rebuilding the existing infrastructure, you add a new, ML-powered abstraction layer on top of the underlying data sources, enabling various users to access and manage the information they need without duplication. Data fabric architecture example. Unified data access. Data catalog.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Maxime Beauchemin

JANUARY 20, 2017

In retrospect, complex SCD modeling techniques are not intuitive and reduce accessibility. It also becomes the role of the data engineering team to be a “center of excellence” through the definitions of standards, best practices and certification processes for data objects.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Edureka

FEBRUARY 7, 2023

Overall, becoming an Azure Data Engineer can provide individuals with a rewarding career path that offers strong earning potential, career growth opportunities, and the chance to work with the latest technologies in a flexible and dynamic environment. How to Become an Azure Data Engineer?

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Monte Carlo

MAY 17, 2023

For example, a one person data team at an insurance company found they were spending more time maintaining tools than actually using them to deliver data. With these bottlenecks and a lack of accessibility to—and therefore trust in—the data, many data consumers found workarounds by simply querying the source data directly.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

For example, a one person data team at an insurance company found they were spending more time maintaining tools than actually using them to deliver data. With these bottlenecks and a lack of accessibility to—and therefore trust in—the data, many data consumers found workarounds by simply querying the source data directly.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52