Precisely

MARCH 4, 2024

As you navigate the complexities of integrating AI into your operations, it’s essential to understand data integrity – data with maximum accuracy, consistency, and context – and its pivotal role in ensuring AI reliability. Next, you’ll see valuable AI use cases and how data integrity powers success.

Amazon Web Services 70

Amazon Web Services Data Integration High Quality Data Government 70

Databand.ai

AUGUST 30, 2023

8 Data Quality Monitoring Techniques & Metrics to Watch Niv Sluzki August 30, 2023 What Is Data Quality Monitoring? Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Databand.ai

JULY 11, 2023

Data Quality Platform: Benefits, Key Features, and How to Choose Eric Jones July 11, 2023 What Is a Data Quality Platform? A data quality platform is a software solution designed to help organizations manage, maintain, and improve the quality of their data.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Monte Carlo

FEBRUARY 27, 2024

If you’re a data engineer experiencing GenAI-induced whiplash, you’re not alone. On one hand, everyone’s talking about whether GenAI’s not-insignificant data engineering skills are going to automate away their jobs. They need robust data pipelines, high-quality data, well-guarded privacy, and cost-effective scalability.

Data Engineering 59

Data Engineering Data Engineer Engineering High Quality Data 59

Ascend.io

FEBRUARY 28, 2024

The rise of generative AI is changing more than just technology; it’s reshaping our professional landscapes — and yes, data engineering is directly experiencing the impact. How does AI recalibrate the workload and priorities of data teams? How can data engineers harness the power of AI?

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Ascend.io

JUNE 8, 2023

Because of its transformative potential, data has graduated from being merely a by-product of business operations to a critical asset in its own right. Is it possible to treat data not just as a necessary operational output, but as a product that holds immense strategic value? Without them, data products can’t exist.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

MAY 25, 2023

Picture this: your data is scattered. Data pipelines originate in multiple places and terminate in various silos across your organization. Your data is inconsistent, ungoverned, inaccessible, and difficult to use. What these companies need—what you might need—is data orchestration. Improved data governance.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. In this article: Why Are Data Observability Tools Important? Faster Troubleshooting Data pipeline failures are expensive and damaging to organizations.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

dbt Developer Hub

JANUARY 23, 2023

Testing the quality of data in your warehouse is an important aspect in any mature data pipeline. One of the biggest blockers for developing a successful data quality pipeline is aggregating test failures and successes in an informational and actionable way. on BigQuery.

Metadata 52

Metadata High Quality Data SQL Data Integration 52

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Place?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Monte Carlo

FEBRUARY 20, 2024

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Times have changed and there are better ways of doing things now.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders. About 61% request you also have a formal computer science degree.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Databand.ai

AUGUST 30, 2023

Building a Winning Data Quality Strategy: Step by Step Eitan Chazbani August 30, 2023 What Is a Data Quality Strategy? A data quality strategy details the processes, tools, and techniques employed to ensure your company’s data is accurate, consistent, complete, and up-to-date.

Building 52

Building Data Cleanse Data Governance Datasets 52

Monte Carlo

JULY 11, 2023

An anomaly certainly given how reliable digital maps are these days, but a data quality error nonetheless, specifically related to data accuracy. A key component of data quality is data accuracy – in fact, it’s one of the 6 overarching dimensions of data quality.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Databand.ai

JANUARY 24, 2023

The Ultimate Guide for Data Teams Eric Jones 2023-01-24 12:21:38 If you find yourself hearing a lot about DataOps and then subsequently asking, “ What is DataOps? The concept of DataOps has become prevalent in recent years as a way to ensure teams effectively manage data and maintain efficient access to high quality, timely data.

Retail 52

Retail Banking Government Manufacturing 52

Monte Carlo

MAY 17, 2023

Data observability, an organization’s ability to fully understand the health and quality of the data in their systems, has become one of the hottest technologies in modern data engineering. Table of Contents Core Data Observability Use Cases 1. Reduce The Amount Of Data Incidents 2. Validate Data 17.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

DataKitchen

JUNE 19, 2023

Our recent survey showed that 97% of data engineers report experiencing burnout in their day-to-day jobs. Given the complicated distributed systems we use to get value from data and the diversity of data, we need a simplifying framework. That idea is the Data Journey.

Data 52

Data Data Validation Utilities High Quality Data 52

Monte Carlo

JUNE 20, 2023

Have you ever come across an internal data product and side-eyed it like it’s your kid’s prom date? While it seems like it fits the requirements, you don’t quite trust it — who knows where the data in this shifty table has been. In our experience, most data products only support one or two use cases.

Building 52

Building Data Datasets High Quality Data 52

Monte Carlo

MAY 17, 2023

Data observability, an organization’s ability to fully understand the health and quality of the data in their systems, has become one of the hottest technologies in modern data engineering. Table of Contents Core Data Observability Use Cases 1. Reduce the amount of data incidents 2. Validate data 17.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

Monte Carlo

AUGUST 30, 2023

Data testing is the first step in many data engineers’ journey toward reliable data. dbt (data build tool) is a SQL-based command-line tool that offers native testing features. dbt also happens to be an incredibly handy tool for running many data quality tests.

SQL 52

SQL Datasets Database High Quality Data 52