Ascend.io

JULY 11, 2024

Additionally, this enables anomaly detection, highlighting irregularities in data that could indicate errors or potential issues. Enabling Data Governance Metadata is vital for compliance and data security. This allows data engineers to focus on strategic tasks, improving productivity, and operational efficiency.

Metadata 52

Metadata IT Government High Quality Data 52

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? We want interoperability for any data stored versus we have to think how to store the data in a specific node to optimize the processing.

Technology 130

Technology Architecture Google Cloud Metadata 130

Monte Carlo

FEBRUARY 22, 2021

Gopi Krishnamurthy, Director of Engineering, leads the team responsible for data engineering, infrastructure, cloud center-of-excellence, growth, and monetization. For Blinkist, having trustworthy and reliable data is foundational to the success of their business. Image courtesy of Blinkist. “At Image courtesy of Blinkist.

Data Pipeline 52

Data Pipeline Data Government Data Engineering 52

Monte Carlo

JUNE 6, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Table of Contents Why is important to answer who is responsible for data quality?

Data Governance 98

Data Governance Government Data Data Engineering 98

Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? The governance team treats every team output as a data product.

Data Governance 75

Data Governance Government Generalist Data Engineering 75

Databand.ai

JULY 19, 2023

However, their importance has grown significantly in recent years due to the increasing complexity of data architectures and the growing need for data governance and compliance. In this article: Why Are Data Lineage Tools Important? Atlan Atlan offers a modern approach to data governance.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Data Engineering Weekly

AUGUST 25, 2024

Take Astro (the fully managed Airflow solution) for a test drive today and unlock a suite of features designed to simplify, optimize, and scale your data pipelines. Try For Free → Conference Alert: Data Engineering for AI/ML This is a virtual conference at the intersection of Data and AI.

Data Engineer 70

Data Engineer Data Engineering Engineering Database-centric 70

Precisely

JANUARY 9, 2023

The pandemic also drove greater demand for self-service models, which allowed more users across the business to leverage data-driven insights, but also made it much harder to spot data quality issues as they occur. As data initiatives become more sophisticated, organizations will uncover new data quality challenges.

Data Integration 81

Data Integration Data Governance Government Data 81

Databand.ai

AUGUST 30, 2023

Besides these categories, specialized solutions tailored specifically for particular domains or use cases also exist, such as ETL (Extract-Transform-Load) tools for managing data pipelines, data integration tools for combining information from disparate sources/systems, and more.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Data Engineering Weekly

MARCH 3, 2024

This approach led to a successful expansion of Copilot access across the engineering team, resulting in a significant increase in productivity and adoption, demonstrating a commitment to enhancing developer experience while maintaining safety and security standards.

Data Engineer 67

Data Engineer Data Engineering Pipeline-centric Engineering 67

Monte Carlo

JANUARY 3, 2024

Let me give you a hint: high-quality proprietary data. It gives the LLM access to that enterprise proprietary data. This is a widely shared sentiment across many data leaders I speak to. If the data team has suddenly surfaced customer-facing, secure data, then they’re on the hook. Snowflake data cloud.

Technology 113

Technology Database Data Governance Data Engineering 113

Snowflake

SEPTEMBER 13, 2023

Snowpark External Access – public preview External Access is in public preview on AWS regions. Users can now easily connect to external network locations, including external LLMs, from their Snowpark code while maintaining high security and governance over their data. Learn more here. Learn more. Learn more.

Python 92

Python SQL Data Pipeline Data Ingestion 92

Ascend.io

OCTOBER 12, 2023

By evaluating the current state of your data ecosystem and establishing explicit objectives, you set the stage for a successful automation transition. Additionally, considerations around data governance and initial workflow design ensure that when you do move forward, you do so with confidence and direction. Where is it stored?

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

Striim

SEPTEMBER 11, 2024

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. We’ll answer the question, “What are data pipelines?” Table of Contents What are Data Pipelines?

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

The Modern Data Company

FEBRUARY 15, 2023

Limited resources: Data management has always been resource intensive, but not all organizations can maintain a full data team. Without suitable resources for company-wide data management, it’s easier to fall behind. DataOS is the world’s first data operating system.

Government 98

Government Data Governance Raw Data Data 98

RandomTrees

FEBRUARY 2, 2024

GPT-based data engineering accelerators make the working of data more accessible. These accelerators use GPT models to do data tasks faster, fix any issues, and save a lot of time. GPT models change data in simple language and also provide summaries and explanations. One can rely on this information.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Pipeline 52

Knowledge Hut

MARCH 20, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Knowledge Hut

MARCH 15, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Data Engineering Podcast

JUNE 2, 2024

Summary Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. At what point does a lack of an explicit governance policy become a liability?

Data Governance 147

Data Governance Government Data Lake High Quality Data 147

Precisely

MAY 13, 2024

A data catalog is a foundational component of any data strategy, providing a go-to knowledge base of data for users across an enterprise. It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Monte Carlo

JULY 15, 2024

Key Responsibilities of Data Owners The primary responsibility of a data owner is ensuring the quality, accuracy, and integrity of their assigned data assets. Career advancement: As organizations become more data-centric, your role as a data owner offers opportunities for career growth.

Pipeline-centric 52

Pipeline-centric Government Data Governance Retail 52

Monte Carlo

JULY 13, 2023

Data-driven organizations generate, collect, and store vast amounts of data. To effectively manage and analyze this data, data engineering teams must navigate a wide range of challenges, including data access, security, compliance, and data observability. Automating self-service access.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Databand.ai

AUGUST 30, 2023

To maintain data integrity, organizations implement various processes and controls, such as data validation, access controls, backups, and audits. Data validation checks help identify errors and inconsistencies in data, while access controls restrict unauthorized users from accessing or modifying data.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

The Modern Data Company

JULY 31, 2023

By omitting the complexity and debt of data pipelines resulting from multiple fragmented tools in the ‘MAD Ecosystem,’ these loops can be efficiently maintained Process Angle : Administering Data Products involves a combination of technology, culture, and process.

Government 98

Government Data Governance Architecture Data Pipeline 98

Ascend.io

DECEMBER 19, 2023

That’s what we call a data pipeline. It could just as well be ‘ELT for Snowflake’ The key takeaway is that these terms are representative of the actual activity being undertaken: the construction and management of data pipelines within the Snowflake environment.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Databand.ai

AUGUST 30, 2023

Besides these categories, specialized solutions tailored specifically for particular domains or use cases also exist, such as extract, transform and load (ETL) tools for managing data pipelines, data integration tools for combining information from disparate sources or systems and more.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Ascend.io

SEPTEMBER 5, 2023

Whether it’s intricate business logic or finely-tuned machine learning models, Snowpark’s UDFs coupled with the embedded Anaconda repository make accessing open-source libraries a breeze. This enhances data governance and aids in decision-making. Stored Procedures : Streamline and orchestrate DataFrame operations.

IT 59

IT Scala Java Programming Language 59

Knowledge Hut

JULY 5, 2024

Cloud Era: Cloud platforms like AWS and Azure took center stage, making sophisticated data solutions accessible to all. Modern Landscape: Today, Data Engineering involves slick ETL processes, real-time streaming, and the concept of data lakes, shaping the backbone of our data-driven world.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Databand.ai

JULY 19, 2023

Data Discovery The purpose of data discovery is to find, understand, and access data from numerous sources. It is the exploratory phase where you identify what data is available, where it is coming from, and how it can be used to benefit your organization. How can we access it? Where is it stored?

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Monte Carlo

JULY 22, 2024

Providing mechanisms to access and compare different versions. By implementing data versioning, you can create a systematic approach to managing the evolution of your data. Why is Data Versioning Important? We define guidelines for version naming, retention, and access control, considering the impact on our ETL processes.

Metadata 52

Metadata Datasets ETL Tools Data 52

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Data Engineering Podcast

APRIL 26, 2021

One of the most common and widely used methods of access is through a business intelligence dashboard. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to data and deliver analytics. RudderStack’s smart customer data pipeline is warehouse-first.

Business Intelligence 100

Business Intelligence Data Warehouse Hadoop Data Pipeline 100

DareData

JULY 5, 2023

The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users. This is the first layer where data may be structured around the typical row-column format. The Data Mart(s) contain the aggregated layer where data is modeled according to specific business needs.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Monte Carlo

JUNE 6, 2023

It’s inevitable that generative AI will disrupt data, too. After speaking with hundreds of data leaders across companies from Fortune 500s to startups, we came up with a few predictions: Access to data will become much easier – and more ubiquitous. Still, like any change, these advancements won’t be without their hurdles.

Data Engineer 84

Data Engineer Data Engineering Engineering Business Intelligence 84

Databand.ai

AUGUST 30, 2023

This may include providing them with access to data quality tools and resources, as well as offering regular training sessions to help them stay up-to-date with the latest best practices in data management. Automate Lineage and Data Governance Data lineage tools can play a crucial role in automating your data quality strategy.

Building 52

Building Data Cleanse Data Governance Datasets 52

Monte Carlo

JUNE 2, 2024

To get a better understanding of their responsibilities, we analyzed 41 job postings on LinkedIn and found these key duties: Designing and Implementing Data Pipelines: The primary responsibility in 90% of the job postings is creating and managing data pipelines.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

To get a better understanding of their responsibilities, we analyzed 41 job postings on LinkedIn and found these key duties: Designing and Implementing Data Pipelines: The primary responsibility in 90% of the job postings is creating and managing data pipelines.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52