LinkedIn Engineering

JUNE 2, 2023

LinkedIn’s members rely on the platform to keep their data secure, and it is essential that the EGRI team takes appropriate measures to ensure that member privacy is protected at all times. DataHub also provides us with a user-friendly interface to monitor metadata and the overall health of our dataset.

Building 75

Building Banking Datasets Media 75

Knowledge Hut

APRIL 20, 2023

The following are some of the key reasons why data governance is important: Ensuring data accuracy and consistency: Data governance helps to ensure that data is accurate, consistent, and trustworthy. This helps organisations make informed decisions based on reliable data.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Databand.ai

AUGUST 30, 2023

In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats. 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

AltexSoft

NOVEMBER 23, 2021

Implementing data virtualization requires fewer resources and investments compared to building a separate consolidated store. Enhanced data security and governance. All enterprise data is available through a single virtual layer for different users and a variety of use cases. ETL in most cases is unnecessary.

Process 69

Process Data Lake Metadata Data Warehouse 69

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

Knowledge Hut

JUNE 30, 2023

DML statements provide the means to interact with the database, perform data analysis, generate reports, and modify data as per the application requirements. Data Sharing and Collaboration: DBMS allows the system to have multiple users or applications to have access to and also change the data concurrently with time.

MySQL 52

MySQL Medical Relational Database Healthcare 52