Hevo

JUNE 7, 2024

Data and time information are essential for accurately capturing, organizing, and processing data for effective data management. These functions help you extract information from the storage system in a logical manner so that you can analyze the data to make informed business decisions.

Data Integration 40

Data Integration Data Data Management Systems 40

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. What factors are most important when building a data management ecosystem?

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. What factors are most important when building a data management ecosystem?

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. What factors are most important when building a data management ecosystem?

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing.

Data Management 52

Data Management Data Lake Management Data Governance 52

Data Engineering Podcast

FEBRUARY 22, 2021

Summary Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short.

Data Integration 100

Data Integration Data Warehouse Data Pipeline BI 100

Data Engineering Podcast

NOVEMBER 20, 2022

In this episode Ian Schweer shares his experiences at Riot Games supporting player-focused features such as machine learning models and recommeder systems that are deployed as part of the game binary. The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it.

Systems 130

Systems Metadata Data Pipeline MongoDB 130

Ascend.io

AUGUST 15, 2023

This same principle holds true in data management. It’s no surprise, then, that the quest for Fivetran alternatives is on the rise as organizations set their sights on a more holistic data approach. Defense: Saving Money with Intelligent Data Refresh In football, a solid defense does more than just stop goals.

Data Management 52

Data Management Management Data Ingestion Data Pipeline 52

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.

Data Integration 52

Data Integration Metadata Data Governance Data 52

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. If you hand a book to a new data engineer, what wisdom would you add to it?

Data Integration 100

Data Integration Coding BI Kafka 100

Data Engineering Podcast

NOVEMBER 20, 2021

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. With the improvements in streaming engines it is now possible to perform all of your data integration in near real time, but it can be challenging to understand the proper processing patterns to make that performant.

Data Lake 100

Data Lake Data Integration Lambda Architecture Process 100

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 Zero-Copy Integration?

Data Integration 100

Data Integration Architecture Data Warehouse Data Lake 100

Towards Data Science

FEBRUARY 6, 2024

ERP and CRM systems are designed and built to fulfil a broad range of business processes and functions. This generalisation makes their data models complex and cryptic and require domain expertise. As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly.

Systems 78

Systems Raw Data Metadata Data Cleanse 78

Precisely

MARCH 23, 2023

You need a flexible framework to efficiently identify, understand, and link the underlying data elements required for accurate, consistent, and contextualized ESG reporting. In summary: your ESG data needs data integrity. Let’s examine that more. Great – but what does that look like by the numbers? What’s your end goal?

Data Integration 52

Data Integration Government High Quality Data Data 52

Data Engineering Podcast

JULY 28, 2019

For a small number of sources it is a tractable problem, but as the overall complexity of the data ecosystem continues to expand it may be time to identify new ways to tame the deluge of information. In this episode Tim Ward, CEO of CluedIn, explains the idea of eventual connectivity as a new paradigm for data integration.

Data Integration 100

Data Integration Metadata Architecture Media 100

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. Data integration with ETL has changed in the last three decades.

AWS 52

AWS Data Management ETL Tools Management 52

The Modern Data Company

APRIL 20, 2023

What is a data operating system? On the surface, it’s an operating system designed specifically for managing and processing large amounts of data. Many products and services have claimed to offer a data operating system in recent years.

Systems 95

Systems Entertainment Government Big Data 95

Databand.ai

JUNE 20, 2023

Niv Sluzki June 20, 2023 What Is Data Integrity? Data integrity refers to the overall accuracy, consistency, and reliability of data stored in a database, data warehouse, or any other information storage system.

Data Integration 52

Data Integration Data Validation Pharmaceutical Data Cleanse 52

Databand.ai

JUNE 21, 2023

Eric Jones June 21, 2023 What Are Data Integrity Tools? Data integrity tools are software applications or systems designed to ensure the accuracy, consistency, and reliability of data stored in databases, spreadsheets, or other data storage systems.

Data Integration 52

Data Integration Data Cleanse Data Validation Telecommunication 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. Visit [link] to learn more.

Data Warehouse 100

Data Warehouse Data Integration Cloud Google Cloud 100

Data Engineering Podcast

JULY 13, 2020

Summary The first stage of every data pipeline is extracting the information from source systems. There are a number of platforms for managing data integration, but there is a notable lack of a robust and easy to use open source option. If you hand a book to a new data engineer, what wisdom would you add to it?

Data Integration 100

Data Integration Data Engineering Data Engineer Data 100

Databand.ai

JULY 6, 2023

Data Integrity Testing: Goals, Process, and Best Practices Niv Sluzki July 6, 2023 What Is Data Integrity Testing? Data integrity testing refers to the process of validating the accuracy, consistency, and reliability of data stored in databases, data warehouses, or other data storage systems.

Data Integration 52

Data Integration Process Data Validation Data Governance 52

Data Engineering Podcast

OCTOBER 5, 2020

Summary Analytical workloads require a well engineered and well maintained data integration process to ensure that your information is reliable and up to date. Building a real-time pipeline for your data lakes and data warehouses is a non-trivial effort, requiring a substantial investment of time and energy.

Data Integration 100

Data Integration Kafka Data Lake Machine Learning 100

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.

Data Integration 52

Data Integration Hospitality Banking Healthcare 52

Knowledge Hut

APRIL 23, 2024

We live in a data-driven culture where familiarity with databases is crucial. Database management is crucial for businesses of all sizes to guarantee that their data is complete, safe, and easily available when needed. Why are Database Management Skills Important? In this section, we'll examine every skill in depth.

Database 52

Database Management Relational Database SQL 52

The Modern Data Company

APRIL 7, 2023

It is important to note that data debt has methodological and technological components. Data management technologies grow old and out of date. The longer an organization ignores its data debt, the worse it will become. Slowed processing also results in frustrated users who stop using the data.

Management 98

Management Government Data Data Management 98

Precisely

MARCH 14, 2023

But early adopters realized that the expertise and hardware needed to manage these systems properly were complex and expensive. Then, data clouds from providers like Snowflake and Databricks made deploying and managing enterprise-grade data solutions much simpler and more cost-effective.

Data Integration 52

Data Integration High Quality Data BI Data 52

Data Engineering Podcast

APRIL 10, 2022

Summary Data engineering is a practice that is multi-faceted and requires integration with a large number of systems. Rivery is a platform designed to reduce this incidental complexity and provide a single system for working across the different stages of the data lifecycle.

Data Integration 100

Data Integration SQL Data Engineering Data Engineer 100

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.

Data Integration 52

Data Integration Banking IT Machine Learning 52

Data Engineering Podcast

JULY 2, 2021

Summary Data integration in the form of extract and load is the critical first step of every data project. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. No more scripts, just SQL.

Data Integration 100

Data Integration SQL BI Data Warehouse 100

Data Engineering Podcast

JULY 3, 2022

In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. Can you describe what the data diff tool is and the story behind it? Closing Announcements Thank you for listening!

Data Integration 100

Data Integration MongoDB Scala MySQL 100

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?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Ascend.io

APRIL 12, 2023

So when we talk about making data usable, we’re having a conversation about data integrity. Data integrity is the overall readiness to make confident business decisions with trustworthy data, repeatedly and consistently. Data integrity is vital to every company’s survival and growth.

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

Grouparoo

NOVEMBER 5, 2021

Companies that can leverage the value embedded within this data will have the best chance of prospering in a competitive and volatile marketplace. This situation is where a data integration process will help. What is Data Integration? In essence, it is integrating data from multiple sources.

Data Integration 40

Data Integration Datasets Data Warehouse Data Consolidation 40

Data Engineering Podcast

MAY 21, 2023

Summary Batch vs. streaming is a long running debate in the world of data integration and transformation. The batch world has been the default for years because of the complexities of running a reliable streaming system at scale. Can you describe what Estuary is and the story behind it?

Data Lake 162

Data Lake Machine Learning Kafka Data Warehouse 162