Jet Global
JULY 2, 2019
To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the data lake. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.
AWS Big Data
OCTOBER 3, 2023
In our previous post Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes , we discussed how you can implement solutions to improve operational efficiencies of your Amazon Simple Storage Service (Amazon S3) data lake that is using the Apache Iceberg open table format and running on the Amazon EMR big data platform.
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AWS Big Data
MARCH 7, 2023
A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.
AWS Big Data
JANUARY 8, 2024
In the subsequent post in our series, we will explore the architectural patterns in building streaming pipelines for real-time BI dashboards, contact center agent, ledger data, personalized real-time recommendation, log analytics, IoT data, Change Data Capture, and real-time marketing data.
Data Virtualization
AUGUST 26, 2020
Recently, we have seen the rise of new technologies like big data, the Internet of things (IoT), and data lakes. But we have not seen many developments in the way that data gets delivered. Modernizing the data infrastructure is the.
Cloudera
SEPTEMBER 23, 2020
Access audits are mastered centrally in Apache Ranger which provides comprehensive non-repudiable audit log for every access event to every resource with rich access event metadata such as: IP. Both fine-grained access control of database objects and access to metadata is provided. Sensitive data identification.
Octopai
MAY 30, 2019
At the most basic level, data catalogs help you organize your company’s massive datasets. Most enterprises have huge data lakes with millions of touchpoints all living in the dark. It’s not enough to simply store customer data in siloed systems; companies need to be able to locate specific metadata points when needed.
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