Data Engineering Weekly

JANUARY 15, 2025

For example: Text Data: Natural Language Processing (NLP) techniques are required to handle the subtleties of human language, such as slang, abbreviations, or incomplete sentences. Images and Videos: Computer vision algorithms must analyze visual content and deal with noisy, blurry, or mislabeled datasets.

Data Engineering 62

Data Engineering Data Engineer Unstructured Data Engineering 62

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.

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Precisely

DECEMBER 12, 2024

Diverse and Rich Historical Data Mainframes store decades’ worth of transactional data. This data captures historical trends and behaviors across different demographics, markets, and socioeconomic conditions. Contextual Insights Historical data from mainframes provides context that is often missing in newer datasets.

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Healthcare Algorithm Finance Data Integration 63

Precisely

JANUARY 9, 2023

With global data creation projected to grow to more than 180 zettabytes by 2025 , it’s not surprising that more organizations than ever are looking to harness their ever-growing datasets to drive more confident business decisions.

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Data Integration Data Governance Government Data 81

Wayne Yaddow

MARCH 28, 2025

First: It is critical to set up a thorough data inventory and assessment procedure. Organizations must do a comprehensive inventory of their current data repositories, recording the data sources, kind, structure, and quality before starting data integration.

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Data Integration Data Governance Datasets Government 52

Precisely

FEBRUARY 9, 2024

In 2023, organizations dealt with more data than ever and witnessed a surge in demand for artificial intelligence use cases – particularly driven by generative AI. They relied on their data as a critical factor to guide their businesses to agility and success. These more complete datasets will both reduce bias and increase accuracy.

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Data Integration Government Data Metadata 68

DataKitchen

NOVEMBER 5, 2024

Finally, the challenge we are addressing in this document – is how to prove the data is correct at each layer.? How do you ensure data quality in every layer? The Medallion architecture is a framework that allows data engineers to build organized and analysis-ready datasets in a lakehouse environment.

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Architecture Raw Data Pipeline-centric Data Ingestion 78

Precisely

APRIL 7, 2025

After my (admittedly lengthy) explanation of what I do as the EVP and GM of our Enrich business, she summarized it in a very succinct, but new way: “Oh, you manage the appending datasets.” We often use different terms when were talking about the same thing in this case, data appending vs. data enrichment.

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Retail Datasets Data Portfolio 74

Ascend.io

OCTOBER 28, 2024

Filling in missing values could involve leveraging other company data sources or even third-party datasets. The cleaned data would then be stored in a centralized database, ready for further analysis. This ensures that the sales data is accurate, reliable, and ready for meaningful analysis.

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Raw Data Datasets Aggregated Data Data Pipeline 52

Striim

AUGUST 31, 2023

Showing how Kappa unifies batch and streaming pipelines The development of Kappa architecture has revolutionized data processing by allowing users to quickly and cost-effectively reduce data integration costs. Stream processors, storage layers, message brokers, and databases make up the basic components of this architecture.

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Data Integration Architecture Amazon Web Services Machine Learning 69

Data Engineering Podcast

JULY 3, 2022

Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Random data doesn’t do it — and production data is not safe (or legal) for developers to use.

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Knowledge Hut

MAY 29, 2024

The choice of datasets is crucial for creating impactful visualizations. Demographic data, such as census data and population growth, help uncover patterns and trends in population dynamics. Economic data, including GDP and employment rates, identify economic patterns and business opportunities. Census Bureau The U.S.

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Datasets Transportation Entertainment Media 52

DataKitchen

FEBRUARY 17, 2025

An open-source AI-driven data quality testing that learns from your data automatically while providing a simple UI, not a code-specific DSL, to review, improve, and manage your data quality test estatea Test Generator. The Challenge of Writing Manual Data Quality Testing Organizations often have hundreds or thousands of tables.

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SQL Python Government Data Engineering 73

Ascend.io

NOVEMBER 14, 2024

These platforms enable scalable and distributed data processing, allowing data teams to efficiently handle massive datasets. Databricks and Apache Spark provide robust parallel processing capabilities for big data workloads, making it easier to distribute tasks across multiple nodes and improve throughput.

Data Pipeline 52

Data Pipeline Amazon Web Services Data Integration Data 52

Engineering at Meta

APRIL 28, 2025

Heuristic rules : handle straightforward, deterministic cases by identifying specific data formats like dates, phone numbers, and user IDs. Machine learning models : trained on labeled datasets using supervised learning and improved through unsupervised learning to identify patterns and anomalies in unlabeled data.

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Precisely

DECEMBER 18, 2024

2025 Outlook: Essential Data Integrity Insights Whats trending in trusted data and AI readiness for 2025? Read the report Poor Address Data is Expensive in More Ways Than One Working with address data comes with unique challenges, and poor-quality data can have far-reaching effects on your business operations.

Data Solutions 58

Data Solutions Retail Datasets Food 58

Netflix Tech

FEBRUARY 14, 2025

Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset. This foundational dataset is essential, as it supports various downstream workflows and enables a multitude of usecases.

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Precisely

NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. AI drives the demand for data integrity.

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Data Analytics Data Governance Data Integration Government 58

Precisely

MARCH 20, 2025

What is data enrichment? Data enrichment is the process of augmenting your organizations internal data with trusted, curated third-party datasets. Its key to delivering the context required to achieve overall data integrity. First, well start with the basics in case a refresher is needed.

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Insurance Datasets Data Programming 68

Pinterest Engineering

NOVEMBER 18, 2024

CDC allows applications to respond to these changes in real-time, making it an essential component for data integration, replication, and synchronization. Real-Time Data Processing : CDC enables real-time data processing by capturing changes as they happen. Why is CDC Important?

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Kafka MySQL Database Software Engineer 50

Precisely

AUGUST 8, 2024

Multi-domain master data management (MDM) is key to breaking down those data silos and gaining one powerful, holistic view of data across all your hubs – providing a “golden record” of master data that supports informed, consistent, and contextually relevant decisions. With a robust approach to data integrity.

Data Integration 52

Data Integration Data Management Data Datasets 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.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Ascend.io

DECEMBER 4, 2024

DataOps emphasizes automation, version control, and streamlined workflows to reduce the time it takes to move data from ingestion to actionable insights. Monitor and Test Data Quality : Build automated testing and monitoring into your data workflows. Scalability: Implement scalable solutions to accommodate growing data volumes.

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Precisely

MAY 13, 2024

Key Takeaways: Data integration is vital for real-time data delivery across diverse cloud models and applications, and for leveraging technologies like generative AI. The right data integration solution helps you streamline operations, enhance data quality, reduce costs, and make better data-driven decisions.

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Data Integration Metadata Amazon Web Services Data Governance 52

phData: Data Engineering

NOVEMBER 8, 2024

The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms.

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Architecture Systems Data Lake Google Cloud 52

Christophe Blefari

OCTOBER 20, 2023

a lea prepare command that creates database objects that needs to be created (dataset, schema, etc.). 25 million Creative Commons image dataset released — Fondant, an open-source processing framework, released publicly available images from web crawling with their associated license. What are the main differences?

Generalist 130

Generalist Entertainment NoSQL Datasets 130

Snowflake

FEBRUARY 12, 2025

Combined with other Snowflake offerings, Cortex Agents now provide an end to end solution for retrieving, processing and governing both structured and unstructured data at scale. Snowflake's support for unstructured data includes capabilities to store, access, process, manage, govern and share such data.

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Unstructured Data Government SQL Structured Data 82

Monte Carlo

JULY 30, 2024

Table of Contents What are Data Quality Dimensions? What are the 7 Data Quality Dimensions? Data Accuracy Data Completeness Data Timeliness Data Uniqueness Data Validity Data Integrity Monitor your Data Quality with Monte Carlo What are Data Quality Dimensions?

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Data Validation Datasets Medical Raw Data 96

Ascend.io

DECEMBER 4, 2024

DataOps emphasizes automation, version control, and streamlined workflows to reduce the time it takes to move data from ingestion to actionable insights. Monitor and Test Data Quality : Build automated testing and monitoring into your data workflows. Scalability: Implement scalable solutions to accommodate growing data volumes.

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Data Pipeline Metadata Data Workflow Data 52

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 187

Hevo

MARCH 28, 2024

Data is your business’s lifeblood. Analyzing business data can help you gain insights to generate reports and create a strategic business plan. If you have large datasets in JSON format, consider migrating them to SQL Server. SQL Server enhances data analysis through its orderly storage structure.

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SQL Data Integration Datasets Data Analysis 52

Cloudera

SEPTEMBER 18, 2024

InDaiX is being evaluated as an extension of Cloudera to include: Datasets Exchange: Industry Datasets: Comprehensive datasets across various domains, including healthcare, finance, and retail. Synthetic Datasets: High-quality synthetic data generated using state-of-the-art techniques, ensuring privacy and compliance.

Datasets 91

Datasets Retail Healthcare Finance 91

Databand.ai

MAY 30, 2023

Data quality can be influenced by various factors, such as data collection methods, data entry processes, data storage, and data integration. Maintaining high data quality is crucial for organizations to gain valuable insights, make informed decisions, and achieve their goals.

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Data Cleanse Datasets Data Governance Data Validation 85

Hevo

MAY 10, 2024

Analyzing vast volumes of data can be challenging. Google BigQuery is a powerful tool that enables you to store, process, and analyze large datasets with ease. However, it may only provide some of the functionalities and tools needed for complex analysis. This is where Databricks steps in.

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Data Integration Datasets Data Process 52

ThoughtSpot

MARCH 5, 2024

When created, Snowflake materializes query results into a persistent table structure that refreshes whenever underlying data changes. These tables provide a centralized location to host both your raw data and transformed datasets optimized for AI-powered analytics with ThoughtSpot.

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BI Datasets SQL Raw Data 111

Maxime Beauchemin

AUGUST 28, 2017

It’s the task of the business intelligence (now data engineering) teams to solve these issues with methodologies that enforces consensus, like Master Data Management (MDM), data integration , and an ambitious data warehousing program.

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Data Engineering Data Engineer Engineering Software Engineer 174

Striim

APRIL 10, 2025

Improved Efficiency and Scalability: Real-time data processing platforms like Striim allow businesses to manage vast datasets without sacrificing performance. This scalability ensures that businesses can handle large, complex datasets efficiently, even as they grow.

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