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Transforming Big Data into Actionable Intelligence

Sisense

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. Big data challenges and solutions.

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Mastering Data Analysis Report and Dashboard

FineReport

In the realm of big data utilization , we often romanticize its profound impact, envisioning scenarios like precision-targeted advertising, streamlined social security management, and the intelligent evolution of the pharmaceutical sector. Why Big Data Analysis Report? Try FineReport Now 1.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

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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. Query the data using Athena Athena is a serverless, interactive analytics service built to analyze unstructured, semi-structured, and structured data where it is hosted.

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

AWS Big Data

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. This concludes creating data sources on the AWS Glue job canvas. Under Transforms , choose SQL Query.

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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

Spark SQL is an Apache Spark module for structured data processing. To run HiveQL-based data workloads with Spark on Kubernetes mode, engineers must embed their SQL queries into programmatic code such as PySpark, which requires additional effort to manually change code. Amazon EMR on EKS release 6.7.0

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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. Dig into AI.