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

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

The Modern Data Company

JANUARY 22, 2024

This mission culminates in the esteemed recognition of honorable mention in Gartner’s 2023 Magic Quadrant for Data Integration, showcasing commitment to excellence and industry leadership in the data-driven era. Data engineering excellence Modern offers robust solutions for building, managing, and operationalizing data pipelines.

Data Integration 87

Data Integration Metadata Government Unstructured Data 87

Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

François Nguyen

JANUARY 18, 2021

To finish the trilogy (Dataops, MLops), let’s talk about DataGovOps or how you can support your Data Governance initiative. In every step,we do not just read, transform and write data, we are also doing that with the metadata. Last part, it was added the data security and privacy part. What data do we have ?

Data Governance 130

Data Governance Metadata Government Data Pipeline 130

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? It provides context for data, making it easier to understand and manage.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Precisely

MAY 23, 2024

Integrated data catalog for metadata support As you build out your IT ecosystem, it is important to leverage tools that have the capabilities to support forward-looking use cases. A notable capability that achieves this is the data catalog. Deployment should be resource-efficient and easily targeted to fit your use cases.

Data Integration 62

Data Integration Metadata Telecommunication Data Governance 62

Data Engineering Podcast

NOVEMBER 26, 2023

Starburst Logo]([link] This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake.

Architecture 147

Architecture Data Lake High Quality Data SQL 147

Data Engineering Podcast

JULY 10, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Monte Carlo

MARCH 13, 2024

Let’s dig in and explore the landscape of the top so-called “data quality tools” — what they are, what they’re not, and whether they’re the right first step towards more reliable data. Governance helps companies set important standards and achieve higher levels of data security, data accessibility, and data quality.

Data Governance 52

Data Governance Government Metadata Data 52

Data Engineering Podcast

JUNE 5, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking all of that information into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Go to dataengineeringpodcast.com/atlan today to learn more about how you can take advantage of active metadata and escape the chaos.

Data Security 100

Data Security Metadata MongoDB Scala 100

Databand.ai

AUGUST 30, 2023

A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Snowflake

DECEMBER 12, 2023

To give customers flexibility for how they fit Snowflake into their data architecture, Iceberg Tables can be configured to use either Snowflake or an external service such as AWS Glue as the table’s catalog to track metadata, with an easy, one-line SQL command to convert the table’s catalog to Snowflake in a metadata-only operation.

Metadata 90

Metadata Python AWS Government 90

Ascend.io

APRIL 7, 2023

With the monolithic architectures most organizations have today, business users are stuck, constantly waiting for new data pipelines to be built or amended based on their requests. Data engineers aren’t huge fans of this paradigm either. Anyone can query the metadata any time anywhere to get the information they need.

Metadata 52

Metadata Datasets Data Governance Government 52

Databand.ai

AUGUST 30, 2023

Poor data quality: The lack of automation and data governance in legacy architectures can lead to data quality issues, such as incomplete, inaccurate, or duplicate data. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. Organizations need to establish data governance policies, processes, and procedures, as well as assign roles and responsibilities for data governance.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Databand.ai

AUGUST 30, 2023

Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively. Data Integration Data integration is the process of collecting, transforming, and consolidating data from various sources.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

RandomTrees

FEBRUARY 6, 2024

This can save time and effort for data engineers, and it can also help to ensure that ETL pipelines are more accurate and reliable. Generative AI with Data Lineage: By automating the process of collecting lineage metadata, generating visualizations of data lineage, and identifying and troubleshooting data lineage problems.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

MAY 18, 2023

Whether you’re a data scientist, data engineer, or business analyst, keeping track of your data’s origin, transformation, and movement is crucial for maintaining transparency, enforcing data governance, and ensuring data quality. Why do you need data lineage?

Metadata 64

Metadata Data Lake Government Data Governance 64

Monte Carlo

JANUARY 16, 2024

Factor in the advertising strategies, media production, partner programming, audience analytics…and you’re looking at an ocean of data that would fill even the deepest trench (we’d like a television show about that too, please!). So how does Fox’s data strategy support these complex data workflows? Image from Castor.

Government 52

Government High Quality Data Data Governance Entertainment 52

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. This may include tasks such as data profiling, data cleansing, and metadata management.

Building 52

Building Data Cleanse Data Governance Datasets 52

François Nguyen

MARCH 7, 2021

In this article, Juan Sequada gives maybe one of the best definition of Data Mesh ” It is paradigm shift towards a distributed architecture that attempts to find an ideal balance between centralization and decentralization of metadata and data management.”

Government 130

Government Data Governance Data Metadata 130

Cloudera

DECEMBER 20, 2021

Understanding that the future of banking is data-driven and cloud-based, Bank of the West embraced cloud computing and its benefits, like remote capabilities, integrated processes, and flexible systems. The platform is centralizing the data, data management & governance, and building custom controls for data ingestion into the system.

Government 89

Government Data Security Banking Metadata 89

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How Apache Iceberg tables structure metadata. Without a central query log, your team can run the risk of data loss and lack of data governance.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Data Engineering Podcast

APRIL 5, 2021

Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Cloudera

OCTOBER 7, 2021

Application Logic: Application logic refers to the type of data processing, and can be anything from analytical or operational systems to data pipelines that ingest data inputs, apply transformations based on some business logic and produce data outputs.

Architecture 124

Architecture Metadata Government Kafka 124

Monte Carlo

JULY 13, 2023

However, to appropriately grant access to the data, data engineers need to know why the users need access to the data and when this access should be revoked. This manual data access process can lead to a bottleneck in the data pipeline , slowing down the process of accessing and utilizing data for decision-making.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? This privacy law must be kept in mind when building data architecture.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Monte Carlo

DECEMBER 8, 2023

Enter: data provenance and data lineage. Data lineage is a visual tool that tracks the movement and transformations of data through various systems, processes, and applications. Data provenance is the record of metadata from data’s original sources, providing the historical context and authenticity of data.

Metadata 52

Metadata Data Data Warehouse Government 52

Monte Carlo

DECEMBER 8, 2023

Enter: data provenance and data lineage. Data lineage is a visual tool that tracks the movement and transformations of data through various systems, processes, and applications. Data provenance is the record of metadata from data’s original sources, providing the historical context and authenticity of data.

Metadata 52

Metadata Data Data Warehouse Government 52

Snowflake

JUNE 27, 2023

Supporting mission criticality with enhanced native data governance, new Snowflake UIs, a growing compliance footprint, and updated cross-cloud business continuity At Snowflake, we’re committed to providing best-in-class native data governance features for customers entrusting our platform with their data.

Data Governance 57

Data Governance Unstructured Data Government SQL 57

The Modern Data Company

JANUARY 17, 2023

These include internal data lakes, apps (1st or 3rd party), web SDK, internal or 3rd-party databases, and even spreadsheets generated within the marketing department. Without lineage or governance metadata, the quality of this data is anyone’s guess. New data is added to the catalog automatically upon ingestion.

Metadata 59

Metadata Government Data Lake Data 59

AltexSoft

NOVEMBER 10, 2021

In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes. At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Databand.ai

JULY 28, 2022

Data lineage instills this confidence by providing clear information about data origin and how data has moved and changed since then. Data lineage provides that understanding to support everything from regulatory compliance to risk management around data breaches. This technique focuses directly on the data (vs.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Cloudera

MARCH 23, 2022

Through Cloudera’s contributions, we have extended support for Hive and Impala, delivering on the vision of a data architecture for multi-function analytics from large scale data engineering (DE) workloads and stream processing (DF) to fast BI and querying (within DW) and machine learning (ML). . 3: Open Performance.

Metadata 90

Metadata Data Architecture BI Machine Learning 90

Data Engineering Podcast

NOVEMBER 9, 2020

In this episode Grant Seward explains how he built Tree Schema to be an easy to use and cost effective option for organizations to build their data catalogs. He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems.

Building 100

Building PostgreSQL BI Metadata 100

Monte Carlo

JANUARY 23, 2024

The speed of ETL and analytics has increased Maxime recalls a time, not too long ago, when data engineering would require running Hive jobs for hours at a time, frequent context switching between jobs and managing different elements of your data pipeline. Data debt or data pipeline chaos.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98