Remove Data Lake Remove Data Transformation Remove Reporting Remove Visualization
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Monitor data pipelines in a serverless data lake

AWS Big Data

The combination of a data lake in a serverless paradigm brings significant cost and performance benefits. By monitoring application logs, you can gain insights into job execution, troubleshoot issues promptly to ensure the overall health and reliability of data pipelines.

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Empower your Jira data in a data lake with Amazon AppFlow and AWS Glue

AWS Big Data

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Search for the Jira Cloud connector.

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Texas Rangers data transformation modernizes stadium operations

CIO Business Intelligence

Noel had already established a relationship with consulting firm Resultant through a smaller data visualization project. Resultant recommended a new, on-prem data infrastructure, complete with data lakes to provide stake holders with a better way to manage data reliability, accuracy, and timeliness.

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

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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What is a Data Pipeline?

Jet Global

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

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7 key Microsoft Azure analytics services (plus one extra)

CIO Business Intelligence

Analytics is the means for discovering those insights, and doing it well requires the right tools for ingesting and preparing data, enriching and tagging it, building and sharing reports, and managing and protecting your data and insights. Azure Data Factory. Azure Data Lake Analytics.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

The data products used inside the company include insights from user journeys, operational reports, and marketing campaign results, among others. The data platform serves on average 60 thousand queries per day. The data volume is in double-digit TBs with steady growth as business and data sources evolve.