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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture. Data governance is a crucial aspect of managing an organization’s data assets.

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AzureML and CRISP-DM – a Framework to help the Business Intelligence professional move to AI

Jen Stirrup

This initial phase focuses on understanding the business value-add from a business perspective, then translating this knowledge into a data mining problem definition. This may also involve the generation of a preliminary plan designed to deliver the business objectives. What are we trying to achieve?

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Data Landscape – Navigating The Data Jungle

Anmut

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success. And that’s important.

ROI 52
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Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue

AWS Big Data

This concludes creating data sources on the AWS Glue job canvas. Next, we add transformations by combining data from these different tables. Transform the data Complete the following steps to add data transformations: On the AWS Glue job canvas, choose the plus sign. Sumitha AP is a Sr.

Sales 52
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How SOCAR handles large IoT data with Amazon MSK and Amazon ElastiCache for Redis

AWS Big Data

Components of the consumer application The consumer application comprises three main parts that work together to consume, transform, and load messages from Amazon MSK into a target database. The following diagram shows an example of data transformations in the handler component. Younggu Yun works at AWS Data Lab in Korea.

IoT 102
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Data Integration Patterns in Knowledge Graph Building with GraphDB

Ontotext

The update is to drop and re-import the same graph data into a single atomic transaction. In use cases when the named graph has other meanings or the granularity of the updates is smaller like on the business object level, the user can design an explicit DELETE/INSERT template.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

Both of these concepts resonated with our team and our objectives, and so we found ourselves supporting both to some extent. Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data.