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SNP Unlocks SAP Data for Advanced Analytics with Its Snowflake Native App

Snowflake

As a cohesive ERP solution, SAP is often one of the largest data resources in an organization, containing everything from financial and transactional data to master information about customers, vendors, materials, facilities, planning and even HR. What’s the challenge with unlocking SAP data?

IT 93
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Deciphering the Data Enigma: Big Data vs Small Data

Knowledge Hut

Big Data vs Small Data: Function Variety Big Data encompasses diverse data types, including structured, unstructured, and semi-structured data. It involves handling data from various sources such as text documents, images, videos, social media posts, and more. How they are Similar?

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Data Warehouse vs Big Data

Knowledge Hut

Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data. Data warehousing offers several advantages. By structuring data in a predefined schema, data warehouses ensure data consistency and accuracy.

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How HomeToGo Is Building a Robust Clickstream Data Architecture with Snowflake, Snowplow and dbt

Snowflake

In this guest blog post, HomeToGo’s director of data, Stephan Claus, explains why the company migrated to Snowflake to meet its data needs. This article is based on Stephan’s presentation during the Snowflake Data World Tour 2022. Something that is especially handy is Snowflake’s support for semi-structured data.

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Why RPA Solutions Aren’t Always the Answer

Precisely

In these scenarios, it’s easy to see how RPA presents a very appealing solution to the inefficiencies and complexities in your master data processes. These include: Structured data dependence: RPA solutions thrive on well-organized, predictable data.

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Business Intelligence vs. Data Mining: A Comparison

Knowledge Hut

Process of analyzing, collecting, and presenting data to support decision-making. Focus Exploration and discovery of hidden patterns and trends in data. Reporting, querying, and analyzing structured data to generate actionable insights. Structured data from databases, data warehouses, and operational systems.

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The Power of Exploratory Data Analysis for ML

Cloudera

Due to the lack of tooling specifically designed for data discovery, exploration, and preliminary analysis, this presents a significant challenge for these teams. . When it comes to the early stages in the data science process, data scientists often find themselves jumping between a wide range of tooling. Next Steps.