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

Acceldata

AUGUST 17, 2023

Bad data delivers bad outcomes. Learn how to avoid bad data and instead, deliver consistently high-quality data through your data pipelines and throughout your environment.

High Quality Data 40

High Quality Data Data Pipeline Data 40

Databand.ai

AUGUST 30, 2023

Data Consistency vs Data Integrity: Similarities and Differences Joseph Arnold August 30, 2023 What Is Data Consistency? Data consistency refers to the state of data in which all copies or instances are the same across all systems and databases. Data consistency is essential for various reasons.

Data Integration 52

Data Integration Data Cleanse Data Validation High Quality Data 52

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

Precisely

MARCH 14, 2023

Then, data clouds from providers like Snowflake and Databricks made deploying and managing enterprise-grade data solutions much simpler and more cost-effective. Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms.

Data Integration 52

Data Integration High Quality Data BI Data 52

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. But what happens when the data is wrong? What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard? The root of data downtime?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 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.

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Data Pipeline Data Lake Data Warehouse Datasets 52

Monte Carlo

MARCH 24, 2023

Imagine a zoo employee entering data on the heights of different animals and they accidentally swap the data for giraffes and penguins. The data has integrity because the information is all there. The heights for both giraffes and penguins have been entered, and there’s no missing or corrupt data.

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Monte Carlo

JANUARY 10, 2024

What happens when you strip away all the noise of queries and pipelines and focus on the data itself? You get down to the intrinsic data quality. What’s the difference between intrinsic and extrinsic data quality? Intrinsic data quality is the quality of data assessed independently of its use case.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Monte Carlo

OCTOBER 4, 2022

In November of 2020, O’Reilly Media first approached us with the idea to author Data Quality Fundamentals: A Practitioner’s Guide to Building More Trustworthy Data Pipelines. It was an inflection point for a fledgling company that had only just begun to establish the category of data observability.

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High Quality Data Data Pipeline Media Data 52

Monte Carlo

JUNE 16, 2023

Data integrity and quality may seem similar at first glance, and they are sometimes used interchangeably in everyday life, but they play unique roles in successful data management. When data has integrity, it means it wasn’t altered during storage, retrieval, or processing without authorization.

Data Integration 52

Data Integration Hospitality Banking Healthcare 52

Monte Carlo

JULY 5, 2023

What does it mean to be truly “data-driven”? All too often, teams tasked with becoming “data-driven” get excited about new technologies (Snowflake! while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. Databricks!)

High Quality Data 52

High Quality Data Data Software Engineer Software Engineering 52

Pipeline Data Engineering

OCTOBER 15, 2021

Data engineering salon. News and interesting reads about the world of data. Re-evaluating Kafka: issues and alternatives for real-time Olivia Iannone Technical Writer at Estuary Kafka’s challenges have exhausted many an engineer on the path to successful data streaming. What if there was an easier way?

High Quality Data 52

High Quality Data Machine Learning Kafka Data Pipeline 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

Ascend.io

JANUARY 10, 2024

Visual representation of Conway’s Law ( source ) Read More: The Chief AI Officer: Avoid The Trap of Conway’s Law Process: Ensuring Data Readiness The backbone of successful AI implementation is robust data management processes. AI models are only as good as the data they consume, making continuous data readiness crucial.

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Databand.ai

AUGUST 30, 2023

Data Accuracy vs Data Integrity: Similarities and Differences Eric Jones August 30, 2023 What Is Data Accuracy? Data accuracy refers to the degree to which data is correct, precise, and free from errors. In other words, it measures the closeness of a piece of data to its true value. What Is Data Integrity?

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 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

JANUARY 3, 2024

Data teams are scrambling to answer the call. That quick check of the box feels like a step forward, but if you aren’t already thinking about how to connect LLMs with your proprietary data and business context to actually drive differentiated value, you’re behind. Hard truth #4: Your data isn’t ready yet anyway.

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Technology Database Data Governance Data Engineering 113

Databand.ai

JULY 17, 2023

DataOps , short for Data Operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. It aims to streamline the entire data lifecycle—from ingestion and preparation to analytics and reporting.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Snowflake

JULY 27, 2023

In this guest blog post, HomeToGo’s director of data, Stephan Claus, explains why the company migrated to Snowflake to meet its data needs. This article is based on Stephan’s presentation during the Snowflake Data World Tour 2022. Over the course of this journey, HomeToGo’s data needs have evolved considerably.

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Data Architecture Architecture Building Structured Data 52

Monte Carlo

DECEMBER 19, 2022

Getting started with data quality testing? Here are the 7 must-have checks to improve data quality and ensure reliability for your most critical assets. Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption.

High Quality Data 52

High Quality Data Data Bytes SQL 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. It’s also important to understand the limitations of data validation testing.

Data Validation 52

Data Validation Data Pipeline SQL Data 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.

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Metadata High Quality Data SQL Data Integration 52

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.

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Unstructured Data Metadata Data Coding 82

DataKitchen

FEBRUARY 18, 2022

DataOps is a discipline focused on the delivery of data faster, better, and cheaper to derive business value quickly. It closely follows the best practices of DevOps although the implementation of DataOps to data is nothing like DevOps to code. Data governance is necessary in the enforcement of Data Privacy.

High Quality Data 52

High Quality Data Business Intelligence Data Pipeline Government 52

Monte Carlo

AUGUST 21, 2023

It looks like it’s time to start thinking about how to structure and support generative AI teams within your data organization. Risks are generally unforeseen and uncertainty is high. Data science teams have encountered all of these issues with their machine learning algorithms and applications over the last five years or so.

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Data Science Machine Learning Media Data 98

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

Monte Carlo

JUNE 12, 2023

We’ve seen it time and time again — change is the only constant for data teams. New customers hungry for data. And, occasionally, an entirely new data platform to meet evolving business needs. All while ensuring data reliability from end-to-end. New sources to ingest. New team structures to support them. Easy, right?

Kafka 52

Kafka Algorithm Data Pipeline High Quality Data 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

Data Engineering Weekly

NOVEMBER 11, 2022

I took a break from data engineering and focused on system monitoring and observability for some time. Little did I know I would be building data pipelines to measure system efficiency instead of measuring business efficiency. As the famous saying goes; Once a data engineer, always a data engineer. No, not at all.

Systems 40

Systems High Quality Data Consulting Metadata 40

Monte Carlo

JANUARY 25, 2023

Over the past year, data contracts have taken the data world by storm as a novel approach to ensuring data quality at scale in production services. In the last couple of posts , I’ve focused on implementing data contracts in production services. Where to start implementing data contracts?

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 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

Monte Carlo

MAY 24, 2022

Four years ago, I was leading a data team at Gainsight, a customer success company, responsible for generating the analytics that powered our executive dashboards. I was regularly getting questions and pings from downstream consumers like “This data is wrong.” Where data observability is today Organizations spent $39.2

High Quality Data 52

High Quality Data Data Lake Media Data 52

Monte Carlo

SEPTEMBER 22, 2022

Making data-driven decisions is critical for the company to “raise the bar” on men’s personal care products according to their VP of Data, IT & Security, Nick Johnson. As a result, Dr. Squatch’s data team focuses on analytics and reporting use cases that provide actionable insights to the broader company. The solution?

High Quality Data 52

High Quality Data Data Data Engineering Data Engineer 52

DataKitchen

NOVEMBER 4, 2021

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

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Process Data Process Pharmaceutical Data Lake 98

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. Join in with the event for the global data community, Data Council Austin.

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Data Lake High Quality Data Data Warehouse Google Cloud 262

Monte Carlo

OCTOBER 5, 2023

A botched ETL job is a ticking time bomb, nestled within the heart of your data infrastructure, waiting to detonate a whirlwind of inaccuracies and inconsistencies. Ensure data quality Even if there are no errors during the ETL process, you still have to make sure the data meets quality standards. What’s ETL?

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Data Cleanse ETL Tools Datasets High Quality Data 52