AWS Big Data
FEBRUARY 1, 2023
We also used AWS Lambda for data processing. To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. Clients access this data store with an API’s.
Smart Data Collective
OCTOBER 13, 2021
Agile BI and Reporting, Single Customer View, Data Services, Web and Cloud Computing Integration are scenarios where Data Virtualization offers feasible and more efficient alternatives to traditional solutions. Does Data Virtualization support web data integration? In forecasting future events.
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erwin
APRIL 29, 2020
Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. For example, the marketing department uses demographics and customer behavior to forecast sales.
Jet Global
JUNE 17, 2021
Although the workbooks were standardized, data entered were not always complete or in line with numbers forecast earlier in the year. The semi-manual approach to data capture also led to inaccuracies that needed to be managed and corrected centrally. Managing Data Integrity. The Need to Free Up Time.
AWS Big Data
MARCH 26, 2024
Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Then, you transform this data into a concise format.
Cloudera
MARCH 23, 2022
Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.
IBM Big Data Hub
OCTOBER 12, 2023
Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.) Learn more about IBM watsonx 1.
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