AWS Big Data

APRIL 19, 2023

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. Instead, we can use automation to speed up the process of migration and reduce heavy lifting tasks, costs, and risks. The script generates a metadata JSON file for each step.

Metadata 82

Metadata Testing Data Lake Consulting 82

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 101

Data Lake Data Warehouse Management Risk 101

AWS Big Data

JANUARY 22, 2024

Data as a product Treating data as a product entails three key components: the data itself, the metadata, and the associated code and infrastructure. In this approach, teams responsible for generating data are referred to as producers. Srikant Das is an Acceleration Lab Solutions Architect at Amazon Web Services.

Data-driven 87

Data-driven Advertising Metadata Data Architecture 87

O'Reilly on Data

MAY 21, 2019

Use ML to unlock new data types—e.g., Consider deep learning, a specific form of machine learning that resurfaced in 2011/2012 due to record-setting models in speech and computer vision. Thus, many developers will need to curate data, train models, and analyze the results of models. A typical data pipeline for machine learning.

Machine Learning 234

Machine Learning Technology Deep Learning Data Science 234

AWS Big Data

APRIL 3, 2024

They recognize the importance of accurate, complete, and timely data in enabling informed decision-making and fostering trust in their analytics and reporting processes. Amazon DataZone data assets can be updated at varying frequencies. You can also update an existing data source to enable data quality.

Data Quality 90

Data Quality Visualization Metadata Metrics 90

Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). We keep feeding the monster data. a second priority?at

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Domino Data Lab

AUGUST 8, 2019

I mention this here because there was a lot of overlap between current industry data governance needs and what the scientific community is working toward for scholarly infrastructure. The gist is, leveraging metadata about research datasets, projects, publications, etc., The probabilistic nature changes the risks and process required.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71