AWS Big Data

DECEMBER 21, 2023

To ingest the data, smava uses a set of popular third-party customer data platforms complemented by custom scripts. After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets.

Data Lake 85

Data Lake Data Warehouse B2B Data-driven 85

AWS Big Data

OCTOBER 18, 2023

Spark SQL is an Apache Spark module for structured data processing. FINRA centralizes all its data in Amazon Simple Storage Service (Amazon S3) with a remote Hive metastore on Amazon Relational Database Service (Amazon RDS) to manage their metadata information. starts_with(OutputKey,'eksclusterEKSConfig')].OutputValue"

Big Data 77

Big Data Data Processing Interactive Testing 77

AWS Big Data

JUNE 6, 2023

In order to mature our data marts, it became clear that we needed to provide Analysts and other data consumers with all tracked digital analytics data in our DWH as they depend on it for analyses, reporting, campaign evaluation, product development and A/B testing. It consists of full-day and intraday tables.

Analytics 55

Analytics Data Lake Testing Optimization 55

Ontotext

JUNE 22, 2023

The long wait comes from the need for extensive testing in order to ensure that a drug is safe and efficient before it can be available to those who need it. They frequently spend hours reading through hundreds of publications to find new insights and then confirm them with structured information.

Metrics 52

Metrics Statistics Visualization Metadata 52

AWS Big Data

MARCH 13, 2024

AWS Glue crawls both S3 bucket paths, populates the AWS Glue database tables based on the inferred schemas, and makes the data available to other analytics applications through the AWS Glue Data Catalog. Athena is used to run geospatial queries on the location data stored in the S3 buckets. Choose Run.

Analytics 90

Analytics IoT Metadata Internet of Things 90

AWS Big Data

NOVEMBER 22, 2023

Data lakes are designed for storing vast amounts of raw, unstructured, or semi-structured data at a low cost, and organizations share those datasets across multiple departments and teams. The queries on these large datasets read vast amounts of data and can perform complex join operations on multiple datasets.

Statistics 98

Statistics Data Lake Optimization Data-driven 98

Ontotext

MAY 26, 2021

In the above case of merging information about companies from different data sources, data linking helps us encode the real-world business logic into data linking rules. But, before we can have any larger scale implementation of these rules, we have to test their validity. How does the Gold Standard help data linking?

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

The Unofficial Google Data Science Blog

SEPTEMBER 9, 2015

Sawzall is a programming language developed at Google for performing aggregation over the result of complex operations on structured data. While use of Sawzall at Google is in decline today, we believe the lessons discussed here have survived the test of time and are employed by descendant systems used throughout Google.

Data Science 56

Data Science Statistics Testing Metadata 56

Cloudera

MAY 17, 2021

Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform. Read the reports , and for more on Cloudera, sign up for a 60 day trial , or test drive CDP.

Data Warehouse 88

Data Warehouse Measurement Reporting Testing 88

IBM Big Data Hub

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data lakehouse was created to solve these problems.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 123

Metadata Cost-Benefit Enterprise Interactive 123