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Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue

AWS Big Data

We create the insert_orders_fact_tbl AWS Glue job manually using AWS Glue Visual Studio. You can use AWS Glue Studio to create jobs that extract structured or semi-structured data from a data source, perform a transformation of that data, and save the result set in a data target.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Visual layouts in some screenshots in this post may look different than those on your AWS Management Console. You’re now ready to query the tables using Athena.

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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.

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Transforming Big Data into Actionable Intelligence

Sisense

Before we dive into the topics of big data as a service and analytics applied to same, let’s quickly clarify data analytics using an oft-used application of analytics: Visualization! As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

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Mastering Data Analysis Report and Dashboard

FineReport

Data Analysis Report (by FineReport ) Note: All the data analysis reports in this article are created using the FineReport reporting tool. Leveraging the advanced enterprise-level web reporting tool capabilities of FineReport , we empower businesses to achieve genuine data transformation. Try FineReport Now 1.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

For the downstream consumption by all departments across the organization, smava’s Data Platform team prepares curated data products following the extract, load, and transform (ELT) pattern. The data products from the Business Vault and Data Mart stages are now available for consumers.