The Modern Data Company

JULY 24, 2023

By chaining data products together, organizations can unlock new levels of data-driven decision-making and drive impactful use cases. Instead of treating data products as isolated entities, organizations can leverage their complementary nature and create a network of interconnected data products.

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Precisely

MAY 31, 2023

To innovate, compete, and grow in the current macroeconomic environment, enterprises must approach data strategically. A sound data strategy doesn’t happen by accident; it’s built on a foundation of data integrity , including accuracy, consistency, and rich context. Many organizations still struggle with data integrity.

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Striim

AUGUST 31, 2023

Treating batch and streaming as separate pipelines for separate use cases drives up complexity, cost, and ultimately deters data teams from solving business problems that truly require data streaming architectures.

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Precisely

JANUARY 9, 2023

With this in mind, below are some of the top trends for data-driven decision-making we can expect to see over the next 12 months. More sophisticated data initiatives will increase data quality challenges Data quality has always been a top concern for businesses, but now the use cases for it are evolving.

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Knowledge Hut

JULY 3, 2023

Analyzing the data in real-time, organizations can detect fraudulent transactions, instrument health, suspicious activity, and unauthorized access attempts hence weakening the risks quickly and allowing protection and smooth operations of processes. Here are some key uses of real-time data ingestion: 1.

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Data Ingestion Pipeline-centric Google Cloud Media 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.

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Data Integration Data Cleanse Data Validation Data Governance 52

KDnuggets

JANUARY 19, 2023

In this article, we will discuss use cases and methods for using ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes along with SQL to integrate data from various sources.

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Knowledge Hut

APRIL 19, 2023

It is the process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. ETL is used to integrate data from different sources and formats into a single target for analysis. What is an ETL Pipeline?

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Monte Carlo

APRIL 20, 2023

What are the data lineage use cases and best practices that will drive value for the business? Here is a list of 17 valuable data lineage use cases that can help improve data quality, incident management, democratization, system modernization, and compliance. We’re glad you asked.

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ProjectPro

JANUARY 27, 2023

Whether you are a data engineer, BI engineer, data analyst, or an ETL developer, understanding various ETL use cases and applications can help you make the most of your data by unleashing the power and capabilities of ETL in your organization. You have probably heard the saying, "data is the new oil".

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Towards Data Science

OCTOBER 12, 2023

Construction engineer investigating his work — Stable diffusion Introduction In our previous publication, From Data Engineering to Prompt Engineering , we demonstrated how to utilize ChatGPT to solve data preparation tasks. In recent decades, data architectures have grown increasingly diverse and complex. This is a fair point.

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Data Engineering Podcast

NOVEMBER 30, 2020

Summary The first stage of every good pipeline is to perform data integration. With the increasing pace of change and the need for up to date analytics the need to integrate that data in near real time is growing. There are a number of projects and platforms on the market that target data integration.

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Monte Carlo

MAY 17, 2023

Because the technology is so extensible, there have been a wide array of suggestions–some more grounded than others–for how it can be used. To help clarify, we reviewed hundreds of data observability deployments to identify 61 real data observability use cases and benefits. Reduce The Amount Of Data Incidents 2.

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Data Engineering Podcast

JULY 28, 2019

Summary The ETL pattern that has become commonplace for integrating data from multiple sources has proven useful, but complex to maintain. In this episode Tim Ward, CEO of CluedIn, explains the idea of eventual connectivity as a new paradigm for data integration. Can you talk through an example use case?

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Data Engineering Podcast

AUGUST 27, 2021

Summary The reason that so much time and energy is spent on data integration is because of how our applications are designed. By making the software be the owner of the data that it generates, we have to go through the trouble of extracting the information to then be used elsewhere. What is it used for?

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Precisely

FEBRUARY 6, 2023

Data drives innovation in today’s global economy. Leading brands use advanced analytics and mobility data to understand consumers better and drive compelling customer experiences. Companies across all industries unify data from disparate sources for innovative AI initiatives that deliver proven value.

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Precisely

JULY 31, 2023

The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.

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Data Integration Telecommunication Data Governance Government 52

Monte Carlo

MAY 17, 2023

Because the technology is so extensible, there have been a wide array of suggestions–some more grounded than others–for how it can be used. To help clarify, we reviewed hundreds of data observability deployments to identify 61 real data observability use cases and benefits. Reduce the amount of data incidents 2.

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Precisely

MARCH 14, 2023

Now, almost any company can build a solid, cost-effective data analytics or BI practice grounded in these new cloud platforms. eBook 4 Ways to Measure Data Quality To measure data quality and track the effectiveness of data quality improvement efforts you need data.

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ProjectPro

DECEMBER 7, 2021

This blog will give you an in-depth knowledge of what is a data pipeline and also explore other aspects such as data pipeline architecture, data pipeline tools, use cases, and so much more. Traditional organizations fail to analyze all the generated data due to a lack of automation.

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Precisely

MARCH 6, 2023

Read Financial Services ESG Reporting: The essential role of data Financial organizations are now assessing how they can derive more environmental, social and governance insights, while optimizing reporting through their existing infrastructure.

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Data Integration Government Data Governance Datasets 52

Snowflake

JANUARY 23, 2023

Marketing data integration is the process of combining marketing data from different sources to create a unified and consistent view. If you’re running marketing campaigns on multiple platforms—Facebook, Instagram, TikTok, email—you need marketing data integration. What Problems does Data Integration Solve?

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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. You can have data quality, without data integrity.

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Grouparoo

DECEMBER 7, 2021

Integrity is a critical aspect of data processing; if the integrity of the data is unknown, the trustworthiness of the information it contains is unknown. What is Data Integrity? Data integrity is the accuracy and consistency over the lifetime of the content and format of a data item.

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Data Integration Manufacturing ETL Tools Transportation 52

Data Engineering Podcast

MAY 1, 2022

Summary The predominant pattern for data integration in the cloud has become extract, load, and then transform or ELT. What are the use cases and user personas that you are focused on supporting? What is a typical workflow for integrating Matillion into an organization and building a set of pipelines?

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Precisely

JULY 21, 2023

Organizations face increasing demands for real-time processing and analysis of large volumes of data. Used by more than 75% of the Fortune 500, Apache Kafka has emerged as a powerful open source data streaming platform to meet these challenges. This is where Confluent steps in.

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Data Integration Kafka Bytes Banking 52

Monte Carlo

MARCH 24, 2023

However, the data is not valid because the height information is incorrect – penguins have the height data for giraffes, and vice versa. The data doesn’t accurately represent the real heights of the animals, so it lacks validity. What is Data Integrity? How Do You Maintain Data Integrity?

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Precisely

JANUARY 25, 2023

Financial institutions are using data in a myriad of different ways, from know-your-customer (KYC) compliance to marketing insights and channel optimization, from risk assessment and fraud detection to innovative AI and machine learning initiatives. This post examines the practical implications of poor data integrity.

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Data Integration Banking IT Machine Learning 52

Hevo

JULY 16, 2023

And one way to enable this is with the use of Streaming SQL. As opposed to traditional SQL, Streaming SQL can help organizations query and analyze data as it […]

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SQL Data Data Pipeline IT 52

Ascend.io

DECEMBER 14, 2022

As data became the backbone of most businesses, data integration emerged as one of the most significant challenges. Today, a good part of the job of a data engineer is to move data from one place to another by creating pipelines that can be either ETL vs. ELT. However, that is less and less the case.

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Analytics Vidhya

AUGUST 22, 2023

Introduction In today’s data-driven world, seamless data integration plays a crucial role in driving business decisions and innovation. Two prominent methodologies have emerged to facilitate this process: Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT).

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Data Integration Process Datasets Data 185

Precisely

MARCH 7, 2024

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. This article focuses on some fundamental prerequisites for the successful use of generative AI in the claims management process. Read to learn more.

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Precisely

NOVEMBER 20, 2023

The answers lie in data integrity and the contextual richness of the data that fuels your AI. They may stop using it, and they’re likely to approach future initiatives with skepticism. Identifying and correcting errors in your data consumes time and resources. Contextual data. Cost savings.

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Raw Data Insurance Data Retail 121

Striim

APRIL 3, 2024

This explosion in cloud application use has led to significant challenges in data integration and the delivery of insightful data to stakeholders. What is Striim Cloud for Application Integration? Enterprises in the U.S. deploy an average of 105 applications , with new applications continuously being adopted.

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Precisely

APRIL 8, 2024

That data has the potential to open doors to vast amounts of useful information and insights. In many cases, hyperlocal deliveries are handled by nonprofessional drivers with less knowledge of local roads and housing developments. To optimize these investments, businesses need good data. Contextual richness adds value.

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Food Retail Data Integration Data 64

Data Engineering Podcast

MAY 21, 2023

Summary Batch vs. streaming is a long running debate in the world of data integration and transformation. In this episode David Yaffe and Johnny Graettinger share the story behind the business and technology and how you can start using it today to build a real-time data lake without all of the headache.

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Data Engineering Weekly

APRIL 7, 2024

We index only top-tier tables, promoting the use of these higher-quality datasets. I strongly believe the concept of Data Product will play a bigger role in data engineering. It is evident that it will become the foundation of trusted sources, which is essential to taking advantage of advancements from LLMs.

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