Remove Data Integration Remove Data Warehouse Remove Predictive Analytics Remove Structured Data
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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

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Improve healthcare services through patient 360: A zero-ETL approach to enable near real-time data analytics

AWS Big Data

Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process. You can send data from your streaming source to this resource for ingesting the data into a Redshift data warehouse.

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Configure end-to-end data pipelines with Etleap, Amazon Redshift, and dbt

AWS Big Data

Introduction to Amazon Redshift Amazon Redshift is a fast, fully-managed, self-learning, self-tuning, petabyte-scale, ANSI-SQL compatible, and secure cloud data warehouse. Thousands of customers use Amazon Redshift to analyze exabytes of data and run complex analytical queries.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

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). Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

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The Data Journey: From Raw Data to Insights

Sisense

In all cases the data will eventually be loaded into a different place, so it can be managed, and organized, using a package such as Sisense for Cloud Data Teams. Using data pipelines and data integration between data storage tools, engineers perform ETL (Extract, transform and load). Connect tables.

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Chose Both: Data Fabric and Data Lakehouse

Cloudera

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making.