Data Integration Data Lake Data Processing Metadata 
AWS Big Data
OCTOBER 3, 2023
A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.
AWS Big Data
DECEMBER 13, 2023
In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.
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AWS Big Data
MARCH 29, 2024
You can slice data by different dimensions like job name, see anomalies, and share reports securely across your organization. With these insights, teams have the visibility to make data integration pipelines more efficient. An AWS Glue crawler scans data on the S3 bucket and populates table metadata on the AWS Glue Data Catalog.
AWS Big Data
JUNE 7, 2023
This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. For this, Cargotec built an Amazon Simple Storage Service (Amazon S3) data lake and cataloged the data assets in AWS Glue Data Catalog.
Domino Data Lab
APRIL 3, 2019
Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. in lieu of simply landing in a data lake.
AWS Big Data
JULY 20, 2023
With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.
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.
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