Airbnb Tech

NOVEMBER 28, 2023

By: Clark Wright Introduction These days, as the volume of data collected by companies grows exponentially, we’re all realizing that more data is not always better. In fact, more data, especially if you can’t rely on its quality, can hinder a company by slowing down decision-making or causing poor decisions.

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Databand.ai

AUGUST 30, 2023

7 Data Testing Methods, Why You Need Them & When to Use Them Helen Soloveichik August 30, 2023 What Is Data Testing? Data testing involves the verification and validation of datasets to confirm they adhere to specific requirements. This is part of a series of articles about data quality.

Data Validation 52

Data Validation Data Integration Data Database 52

Knowledge Hut

MARCH 18, 2024

How you track and analyze project data is a critical component that can make all the difference in whether a project you manage succeeds or fails. Work performance data is even more important than this process, which is necessary. What is Work Performance Data (WPD)? It becomes work performance information after analysis.

Raw Data 52

Raw Data Data Designing Project 52

Monte Carlo

MAY 31, 2023

In a data-driven world, data integrity is the law of the land. And if data integrity is the law, then a data quality integrity framework is the FBI, the FDA, and the IRS all rolled into one. Because if we can’t trust our data, we also can’t trust the products they’re creating. What is a Data Integrity Framework?

Building 52

Building Data Validation Healthcare Data Integration 52

Monte Carlo

JULY 10, 2023

Making decisions based on incomplete data is like baking a cake with missing ingredients: not a good idea. The same is true with data. If all the information in a data set is accurate and precise, but key values or tables are missing, your analysis won’t be effective. That’s where the definition of data completeness comes in.

Data Collection 40

Data Collection Data Governance Government Data 40

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. In other words, bad data is bad for business.”

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

ProjectPro

NOVEMBER 30, 2021

If you are into Data Science or Big Data, you must be familiar with an ETL pipeline. This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. How do we transform this data to get valuable insights from it?

Process 52

Process Data Pipeline Data Warehouse AWS 52

Ascend.io

OCTOBER 12, 2023

In today’s data-driven world, while many understand the concept of data automation, few grasp the intricacies of putting it into practice. A Note on Our Perspective of Data Automation When we talk about “automating data” in this article, our intent isn’t to promote the notion of replacing human involvement.

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

Databand.ai

NOVEMBER 4, 2022

Re-Imagining Data Observability Ryan Yackel 2022-11-04 10:36:35 Data observability has become one of the hottest topics of the year – and for good reason. Data observability provides an end-to-end view into exactly what’s happening with data pipelines across an organization’s data fabric.

Data 52

Data Data Pipeline Retail Metadata 52

Knowledge Hut

JANUARY 19, 2024

Business Intelligence Analyst Importance The proliferation of IoT-connected objects, IoT-based sensors, rising internet usage, and sharp increases in social media activity are all enhancing businesses' ability to gather enormous amounts of data. What Does a Business Intelligence Analyst Do?

Business Intelligence 52

Business Intelligence BI Business Analyst Finance 52

Monte Carlo

OCTOBER 5, 2023

It’s a big moment for big data. Advancements in AI are generating countless headlines and breaking adoption records , and talented data specialists may be the hottest hires on the market. But without quality data, even the best technologies and teams won’t be able to drive meaningful results. What are data quality standards?

Government 52

Government Data Governance Data Datasets 52

U-Next

SEPTEMBER 7, 2022

A firm can benefit immensely from maintaining accurate and clean employee data, as it is still a difficult task. . Access to employee data and information is essential for efficient staff management. Maintaining communication with your staff, which necessitates correct employee data , is one approach to improve it. .

Data Management 52

Data Management Management Electronics Unstructured Data 52

ProjectPro

JUNE 18, 2015

As many big data companies ramp up huge investments in big data to capture business insights by scrambling to employ data scientists , data engineers and data analysts-big data crowdsourcing can add value to an organizations investment plans. When we think of big data, we think of enterprise crowdsourcing.

Big Data 52

Big Data Process Data Cleanse Data Analytics 52

Rockset

FEBRUARY 24, 2023

Introduction Let’s get this out of the way at the beginning: understanding effective streaming data architectures is hard, and understanding how to make use of streaming data for analytics is really hard. Stream processing or an OLAP database? What Is Streaming Data? Streaming data has been around for decades.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Grouparoo

DECEMBER 7, 2021

Organizations collect and leverage data on an ever-expanding basis to inform business intelligence and optimize practices. Data allows businesses to gain a greater understanding of their suppliers, customers, and internal processes. What is Data Integrity? This is distinct from factors such as data quality.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

Grouparoo

NOVEMBER 29, 2021

The data source is the location of the data that the processing will consume for data processing functions. This can be the point of origin of the data, the place of its creation. Alternatively, this can be data generated by another process and then made available for subsequent processing.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

Grouparoo

OCTOBER 6, 2021

It was a fun experience and I think we made a good choice by picking 97 Things Every Data Engineer Should Know. This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Ascend.io

JULY 19, 2023

Ready or not, data mesh is fast becoming an indispensable part of the data landscape. As data leaders, the question isn’t if you’ll cross paths with this emerging architectural pattern. Consider this your primer to stop overthinking, start acting, and truly harness the power of data mesh.

Data Governance 40

Data Governance Government Metadata Data 40

DataKitchen

JANUARY 23, 2023

What is data lineage? Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. What about DataOps Observability? How does it compare?

Data Governance 52

Data Governance Government Data Pipeline Data 52

dbt Developer Hub

APRIL 24, 2023

Alteryx is a visual data transformation platform with a user-friendly interface and drag-and-drop tools. Nonetheless, Alteryx may have difficulties to cope with the complexity increase within an organization’s data pipeline, and it can become a suboptimal tool when companies start dealing with large and complex data transformations.

BI 83

BI Data Workflow SQL Data Pipeline 83

ProjectPro

JANUARY 31, 2023

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. Get ready to expand your knowledge and take your big data career to the next level! “Data analytics is the future, and the future is NOW!

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

APRIL 19, 2021

Does the idea of evaluating, processing, analyzing, and interpreting statistical data makes you roll up your sleeves and get the job done? Do you love to distinguish the trends and patterns in data? If you answered yes to one or more of these questions, then a career as a data analyst may be your calling.

Portfolio 40

Portfolio Data Finance Certification 40

ProjectPro

AUGUST 21, 2015

With the help of ProjectPro’s Hadoop Instructors, we have put together a detailed list of big data Hadoop interview questions based on the different components of the Hadoop Ecosystem such as MapReduce, Hive, HBase, Pig, YARN, Flume, Sqoop , HDFS, etc. Processes structured data. Give example.

Hadoop 40

Hadoop Big Data Metadata Java 40