This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Azure Data Lakes are highly complex and designed with a different fundamental purpose in mind than financial and operationalreporting. For more on Azure Data Lakes, download this guide: “ Diving into Data Lakes: Is Microsoft’s Modern Data Warehouse Architecture Right for Your Business? ”.
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. There are virtually no rules about what such data looks like. It is unstructured.
Reporting: A Few Technical Basics. Financial and operationalreports retrieve master data and transactional information from your ERP databases using something called “SQL.” That stands for “structured query language.” Data lakes are oriented toward unstructureddata and artificial intelligence.
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.
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.
When extracting your financial and operationalreportingdata 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.
The architecture may vary depending on the specific use case and requirements, but it typically includes stages of data ingestion, transformation, and storage. Data ingestion methods can include batch ingestion (collecting data at scheduled intervals) or real-time streaming data ingestion (collecting data continuously as it is generated).
This is particularly valuable for teams that require instant answers from their data. Data Lake Analytics: Trino doesn’t just stop at databases. It directly queries structured and semi-structureddata from data lakes , enabling operational dashboards and real-time analytics without the need for preprocessing.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content