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

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Smart DwH Mover helps in accelerating data warehouse migration.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

AltexSoft

NOVEMBER 23, 2021

The responsibility of this layer is to access the information scattered across multiple source systems, containing both structured and unstructured data , with the help of connectors and communication protocols. Data virtualization platforms can link to different data sources including.

Process 69

Process Data Lake Metadata Data Warehouse 69

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

AUGUST 21, 2015

Data can either be ingested through batch jobs that run every 15 minutes, once every night and so on or through streaming in real-time from 100 ms to 120 seconds. ii) Data Storage – The subsequent step after ingesting data is to store it either in HDFS or NoSQL database like HBase.

Hadoop 40

Hadoop Big Data Metadata Java 40