Towards Data Science

OCTOBER 12, 2023

To gather all the necessary information we need to infere a Database Schema to ChatGPT including example datasets and field descriptions by using few-shot prompting. We will start out propagating the Database Schema and some example data to ChatGPT.

Data Integration 97

Data Integration Systems Engineering Datasets 97

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.

Data Analytics 72

Data Analytics Data Governance Government Data Integration 72

ProjectPro

JUNE 6, 2025

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 66

AWS Scala Metadata Data Lake 66

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

ProjectPro

JUNE 6, 2025

A report by ResearchAndMarkets projects the global data integration market size to grow from USD 12.24 This growth is due to the increasing adoption of cloud-based data integration solutions such as Azure Data Factory. What is Azure Data Factory? billion in 2020 to USD 24.84 billion by 2025, at a CAGR of 15.2%

Data Lake 60

Data Lake Metadata SQL Datasets 60

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 Engineer 62

Data Engineer Data Engineering Unstructured Data Engineering 62

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.

Data Integration 81

Data Integration Data Governance Government Data 81

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.

Healthcare 64

Healthcare Finance Algorithm Data Integration 64

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.

Data Integration 69

Data Integration Government Food Metadata 69

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.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

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.

Data Governance 52

Data Governance Government Data Integration Datasets 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 ETL System 69

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.

Retail 75

Retail Datasets Data Telecommunication 75

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.

Data Integration 100

Data Integration MongoDB Scala MySQL 100

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.

Raw Data 52

Raw Data Aggregated Data Data Pipeline Data Validation 52

ProjectPro

JUNE 6, 2025

1) Build an Uber Data Analytics Dashboard This data engineering project idea revolves around analyzing Uber ride data to visualize trends and generate actionable insights. This project will help analyze user data for actionable insights. Utilize the Spotify Million Playlist Dataset to study user listening patterns.

Data Engineer 59

Data Engineer Data Engineering Project Engineering 59

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.

SQL 74

SQL Python Government Data Engineer 74

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?

Kafka 56

Kafka MySQL Database Software Engineer 56

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 Data Integration 52

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.

Data Analytics 59

Data Analytics Data Governance Government Data Integration 59

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 59

Data Solutions Retail Food Datasets 59

ProjectPro

JUNE 6, 2025

Traditional databases may need help to provide the necessary performance when dealing with large datasets and complex queries. Data warehousing tools are designed to handle such scenarios efficiently, enabling faster query performance and analysis, even on massive datasets. Familiar SQL language for querying.

Data Storage 58

Data Storage PostgreSQL Data Warehouse AWS 58

ProjectPro

JUNE 6, 2025

Features of Apache Spark Allows Real-Time Stream Processing- Spark can handle and analyze data stored in Hadoop clusters and change data in real time using Spark Streaming. Spark uses Resilient Distributed Dataset (RDD), which allows it to keep data in memory transparently and read/write it to disc only when necessary.

Data Engineer 45

Data Engineer Data Engineering Engineering Kafka 45

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.

Kafka 67

Kafka Datasets Metadata Utilities 67

ProjectPro

JUNE 6, 2025

Why do data engineers love Azure Data Factory? Is Azure Data Factory Real-Time? What is Azure Data Factory? You can use Azure Data Factory to construct and plan data-driven processes (also known as pipelines) that can consume data from many sources.

Data Engineer 40

Data Engineer Data Engineering Engineering Hospitality 40

ProjectPro

JUNE 6, 2025

Let us understand how this Microsoft Azure Data Factory Training will benefit data engineers in different ways- Comprehensive Understanding of Azure Data Factory- The training provides beginners with a solid foundation in understanding Azure Data Factory's core concepts and functionalities.

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Data Lake Cloud Computing Data Workflow Data Pipeline 40

Precisely

DECEMBER 5, 2024

Key Takeaways: Data enrichment is the process of appending your first-party data with contextually rich third-party data, enabling you to make more data-driven decisions. Third-party data should be relevant, consistent, accessible, and trustworthy. Is data complete across pertinent geographies?

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Insurance Datasets Retail Data 69

ProjectPro

JUNE 6, 2025

This is where ETL tools, such as Talend, come in handy as they help businesses manage cloud and data integration activities efficiently. If you are willing to enter the big data industry and searching for some good Talend projects for resume, you must explore some of the unique Talend ETL projects in this blog.

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Project ETL Tools MySQL Banking 40

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 Manufacturing 69

ProjectPro

JUNE 6, 2025

Here is the list of AWS Data pipeline tools, designed for scalability and efficiency in handling data processing tasks within the AWS ecosystem. Data Catalog : Its integrated data catalog automatically discovers and catalogs metadata from various sources, making it easy to find and understand datasets.

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Data Pipeline Google Cloud Kafka AWS 49

ProjectPro

JUNE 6, 2025

With the rapid growth of data in the industry, businesses often deal with several challenges when handling complex processes such as data integration and analytics. This increases the demand for big data processing tools such as AWS Glue.

AWS 40

AWS IT Data Lake BI 40

ProjectPro

JUNE 6, 2025

As Databricks has revealed, a staggering 73% of a company's data goes unused for analytics and decision-making when stored in a data lake. Built on datasets that fail to capture the majority of a company's data, these models are doomed to return inaccurate results. Write data in delta format by using the below command.

Data Lake 63

Data Lake Data Warehouse Metadata Unstructured Data 63

DataKitchen

NOVEMBER 27, 2024

Similarly, data teams might struggle to determine actionable steps if the metrics do not highlight specific datasets, systems, or processes contributing to poor data quality. This approach allows enterprises to hold data suppliers accountable or optimize their ingestion processes to ensure higher data integrity.

Banking 86

Banking Data Pharmaceutical Consulting 86

ProjectPro

JUNE 6, 2025

Target Data Completeness This involves validating the presence of expected records and the population of required fields in the target dataset, preventing data loss and supporting comprehensive analysis. Record Completeness: Record completeness checks assess whether all expected records are present in the target dataset.

Building 40

Building High Quality Data Datasets Hadoop 40

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.

Data Pipeline 52

Data Pipeline Metadata Data Workflow Data 52

ProjectPro

JUNE 6, 2025

This influx of data and surging demand for fast-moving analytics has had more companies find ways to store and process data efficiently. This is where Data Engineers shine! The first step in any data engineering project is a successful data ingestion strategy. What are the parameters involved in Data Ingestion?

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Data Ingestion Data Engineer Data Engineering Project 45

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 92