AWS Big Data

JULY 19, 2023

We’ve seen that there is a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With this connector, you can bring the data from Google Cloud Storage to Amazon S3.

Big Data 82

Big Data Software Consulting Unstructured Data 82

AWS Big Data

OCTOBER 20, 2023

We’ve seen a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With these connectors, you can bring the data from Azure Blob Storage and Azure Data Lake Storage separately to Amazon S3. Learn more in README.

Data Lake 85

Data Lake Big Data Consulting Data Warehouse 85

Jet Global

SEPTEMBER 4, 2020

There has been a lot of talk over the past year or two in the D365F&SCM world about “data lakes.” Data lakes serve a fundamentally different purpose than data warehouses, in the sense that they are optimized for extremely high volumes of data that may or may not be structured.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

AWS Big Data

DECEMBER 21, 2023

The data products used inside the company include insights from user journeys, operational reports, and marketing campaign results, among others. The data platform serves on average 60 thousand queries per day. The data volume is in double-digit TBs with steady growth as business and data sources evolve.

Data Lake 90

Data Lake Data Warehouse Data-driven B2B 90

Jet Global

NOVEMBER 7, 2023

When extracting your financial and operational reporting data from a cloud ERP, your enterprise organization needs accurate, cost-efficient, user-friendly insights into that data. While real-time extraction is historically faster, your team needs the reliability of the replication process for your cloud data extraction.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52

Jet Global

MAY 9, 2024

ELT can simplify the data pipeline by reducing the number of steps and tools involved, but it may rely on the destination system’s ability to handle complex transformations effectively. In an ETL process, data transformations can be optimized before loading, which may improve performance for data-intensive transformations.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52