Monte Carlo

AUGUST 30, 2023

Your test passes when there are no rows returned, which indicates your data meets your defined conditions. Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. This will help you debug any failures and understand the health of your data pipeline over time.

SQL 52

SQL Datasets Database High Quality Data 52

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

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 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

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 89

Metadata Python AWS Government 89

AltexSoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. How systems exchange data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. The essential components of the virtual layer are.

Process 69

Process Data Lake Metadata Data Warehouse 69

AltexSoft

JULY 29, 2022

A master node called NameNode maintains metadata with critical information, controls user access to the data blocks, makes decisions on replications, and manages slaves. Instruments like Apache ZooKeeper and Apache Oozie help better coordinate operations, schedule jobs, and track metadata across a Hadoop cluster. Let’s see why.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

NOVEMBER 30, 2021

If you are into Data Science or Big Data, you must be familiar with an ETL pipeline. This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. Table of Contents What is ETL Pipeline?

Process 52

Process Data Pipeline Data Warehouse AWS 52

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. Key Design Principles of a Data Mesh.

Architecture 124

Architecture Metadata Government Kafka 124

RandomTrees

FEBRUARY 6, 2024

Transformation: Shaping Data for the Future: LLMs facilitate standardizing date formats with precision and translation of complex organizational structures into logical database designs, streamline the definition of business rules, automate data cleansing, and propose the inclusion of external data for a more complete analytical view.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

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. The data warehouse is the mirror of the organization in many ways.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

Monte Carlo

JANUARY 23, 2023

Data lineage , an automated visualization of the relationships for how data flows across tables and other data assets, is a must-have in the data engineering toolbox. Not only is it helpful for data governance and compliance use cases, it also plays a starring role as one of the 5 pillars of data observability.

IT 52

IT BI Government Data Governance 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

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

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

AltexSoft

MARCH 14, 2023

While the open-source part is definitely a good thing, it requires a high level of expertise as there’s often no GUI. That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise.

IT 59

IT Data Warehouse Data Governance Data Lake 59

phData: Data Engineering

JULY 18, 2023

Better Transparency: There’s more clarity about where data is coming from, where it’s going, why it’s being transformed, and how it’s being used. Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

AltexSoft

MARCH 30, 2023

Shell, Adobe, Burberry, Columbia, Bayer — you definitely know the names. The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

JULY 30, 2020

Unlike traditional monolithic data infrastructures that handle the consumption, storage, transformation, and output of data in one central data lake, a data mesh supports distributed, domain-specific data consumers and views “data-as-a-product,” with each domain handling their own data pipelines.

IT 45

IT Data Lake Business Intelligence Data Architecture 45