Data Engineering Weekly

APRIL 14, 2024

Discover how a universal semantic layer is transforming modern business intelligence, making data more accessible and reliable for organizations striving for informed business decisions. The highlight of the design is the unique blending of batch and real-time events with the common feature definition.

Data Engineering 64

Data Engineering Data Engineer Engineering Business Intelligence 64

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

Monte Carlo

FEBRUARY 9, 2024

“If we can quantify the quality of our data that’s used in these semantic models and define these semantic definitions, then we can start enabling our non-technical users to query from that using Co-Pilot,” says Mike. He believes that potential problems arise when there are varying definitions across the organization.

Data 64

Data High Quality Data Architecture Coding 64

Monte Carlo

MAY 3, 2024

But even though the data landscape is evolving, many enterprise data organizations are still managing data quality the “old” way: with simple data quality monitoring. The basics haven’t changed: high-quality data is still critical to successful business operations.

High Quality Data 52

High Quality Data Data Architecture Media 52

DataKitchen

NOVEMBER 27, 2024

Unlike regulated industries, where the importance of specific data elements is externally prescribed, non-regulated industries must internally determine which data elements are critical to their success. For instance, a marketing team might prioritize customer segmentation data, while operations teams focus on inventory data.

Banking 87

Banking Data Datasets Retail 87

Towards Data Science

JUNE 16, 2023

In the following examples, we’ll be using Looker, but most modern BI tools enable usage-based reporting in some form (Lightdash also has built in Usage Analytics , Tableau Cloud offers Admin Insights , and Mode’s Discovery Database offers access to usage data, just to name a few). yml file alongside your data model definition.

BI 73

BI Data ETL Tools Machine Learning 73

Precisely

SEPTEMBER 18, 2024

Key Data Integrity Trends and Insights for 2025 AI success is impacted by a lack of data readiness The potential of AI has had the business world buzzing – and for good reason. And yet, only 12% of organizations report that their data is of sufficient quality and accessibility for effective AI implementation.

Data Integration 72

Data Integration Data Governance Government Data Programming 72

Precisely

SEPTEMBER 26, 2024

Data integration struggles = biased outcomes Ensuring comprehensive data integration across your organization is often the first stumbling block. If your business operates with fragmented data across silos, then your AI models are working with incomplete or inconsistent datasets. The impact?

Project 75

Project Government Data Governance Data Integration 75

Monte Carlo

JANUARY 23, 2024

As the machine became smarter with the next generation of data access, we flattened everything to features and columns. Almost every business I walk into, you can talk to the marketing team and the sales team and they have different definitions of things with the same name,” John says. “I Max agrees. It’s hard with RAG.

Database-centric 52

Database-centric Pipeline-centric Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 23, 2024

In addition, Snow f lake Cortex enables organizations to quickly analyze data and build AI applications directly within Snowflake. Combining these new developments with tools like Monte Carlo, a Snowflake Horizon partner , means Snowflake customers can accelerate the adoption of high quality data for their AI models.

Systems 64

Systems Building Retail Data Lake 64

Monte Carlo

OCTOBER 10, 2024

All these processes require well-governed access to high-quality data for reporting, analytics, and AI modeling to generate insights that improve the overall customer experience. As the company’s data volume and number of data sources grew, the data quality and metrics team faced several challenges.

High Quality Data 40

High Quality Data Retail Government Data Science 40

Towards Data Science

APRIL 6, 2023

Principles, practices, and examples for ensuring high quality data flows Source: DreamStudio (generated by author) Nearly 100% of companies today rely on data to power business opportunities and 76% use data as an integral part of forming a business strategy.

Unstructured Data 76

Unstructured Data Metadata Data Coding 76

Databand.ai

AUGUST 30, 2023

To maintain data integrity, organizations implement various processes and controls, such as data validation, access controls, backups, and audits. Data validation checks help identify errors and inconsistencies in data, while access controls restrict unauthorized users from accessing or modifying data.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 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

Airbnb Tech

NOVEMBER 24, 2020

A Multi-dimensional Challenge While all stakeholders agreed that data quality was important, employee definitions of “data quality” encompassed a constellation of different issues. These included: Accuracy: Is the data correct? Consistency: Is everybody looking at the same data?

Data Warehouse 52

Data Warehouse Certification Data Pipeline Data 52

DareData

JULY 29, 2020

Odds are that your local hospital, pharmacy or medical institution's definition of being data-driven is keeping files in labelled file cabinets, as opposed to one single drawer. This is why I believe experience play an important role in developing good data pipelines.

Data Pipeline 52

Data Pipeline Healthcare Medical Pipeline-centric 52

Monte Carlo

AUGUST 10, 2023

What is Data observability? The five pillars My data observability definition has not changed since I first coined it in 2019: Data observability refers to an organization’s comprehensive understanding of the health and performance of the data within their systems. It is still relevant today.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Monte Carlo

JUNE 16, 2023

Data quality has broad applications across industries, but its importance and degree of quality required is also contextual to the use case. For example, in marketing, high-quality data can help businesses better understand their customers, allowing them to create more targeted and effective campaigns.

Data Integration 52

Data Integration Hospitality Banking Healthcare 52

Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? How Do You Maintain Data Integrity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Monte Carlo

FEBRUARY 23, 2024

In addition, Snow f lake Cortex enables organizations to quickly analyze data and build AI applications directly within Snowflake. Combining these new developments with tools like Monte Carlo, a Snowflake Horizon partner , means Snowflake customers can accelerate the adoption of high quality data for their AI models.

Systems 52

Systems Building Retail Data Lake 52

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

Ascend.io

JUNE 8, 2023

Data sharing goes beyond simply making the data available. It can be thought of as the final “packaging” and disseminating of the data in a format that meets the specifications of different stakeholders, and is accessible, understandable, and usable across the organization. But how do you unlock these capabilities?

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 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

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 Structured Data 52

Monte Carlo

AUGUST 31, 2023

Your downstream data consumers including product analysts, marketing leaders, and sales teams rely on data-driven tools like CRMs, CXPs, CMSs, and any other acronym under the sun to do their jobs quickly and effectively. But what happens when the data is wrong? What is Data Quality?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 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

Monte Carlo

FEBRUARY 8, 2024

Vendors who specialize in the detection of data reliability issues did not perform as well as the others.” G2 Crowd G2 was one of the earliest non-vendor resources to put together a credible list of data observability vendors and a definition for the category. Robust role based access controls?

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

APRIL 24, 2023

As we saw in Part 2 of our series , the definition of “building” and “buying” can change based on what layer of the data stack we’re considering. Unlike manual SQL writing which is generally limited to data engineers, dbt’s modular SQL makes it possible for anyone familiar with SQL to create their own data transformation jobs.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

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

ProjectPro

MAY 13, 2016

For data scientists or data analysts, Hadoop and EDW are two different but great combinations that taste great when used together. Exponential growth of big data, challenges the scalability and cost factors of a data warehouse because the licence cost models in a data warehousing system are based on the number of CPUs.

Data Warehouse 40

Data Warehouse Hadoop Unstructured Data Big Data 40

ProjectPro

FEBRUARY 21, 2023

Although challenging, a career in data engineering can be rewarding. Data engineers and their skills play a crucial role in the success of an organization by making it easier for data scientists , data analysts , and decision-makers to access the data they need to do their jobs.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

AUGUST 8, 2023

The Definitive Guide to Data Validation Testing Data validation testing ensures your data maintains its quality and integrity as it is transformed and moved from its source to its target destination. From this perspective, the data validation process looks a lot like any other DataOps process.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Towards Data Science

MARCH 29, 2023

Turbo and GPT-4 to categorize datasets without the need for labeled data or model training, by prompting the model with data excerpts and category definitions. Oh, and I also recently got early access to GPT-4 and wanted to take it for a bit of a spin! ? … TL;DR In this article, I explore using GPT-3.5-Turbo

Datasets 61

Datasets Food Retail Hospitality 61

Monte Carlo

MARCH 28, 2024

A data product, coined by DJ Patil, Former Chief Data Scientist of the United States, has several components, including a product management process, the domain wrapper comprising a semantic layer, business logic and metrics, and access. Simply put, a data product is much more than a dataset alone. Who needs this data?

Data 52

Data High Quality Data Machine Learning Accessible 52

Monte Carlo

MARCH 23, 2022

Their primary use case for data observability was so they could make bold, aggressive marketing investments with confidence in order to hit an ambitious 40% growth target. Data quality sets your product apart from competitors With Choozle, advertisers can see campaign performance across every zip code in the United States if they’d like.

IT 52

IT Machine Learning High Quality Data Data 52