Remove Data Processing Remove Data Science Remove Optimization Remove Unstructured Data
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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How to Take Back 40-60% of Your IT Spend by Fixing Your Data

Ontotext

This is partly because integrating and moving data is not the only problem. The data itself is stored in a way that is not optimal for extracting insight. Unlocking additional value from data requires context, relationships, and structure, none of which are present in the way most organizations store their data today.

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COVID-19 Effects on Financial Services & Managing Risk

bridgei2i

How much will the bank’s bottom line be impacted depends on a host of unknowns. Better Forecasting and Optimization. They also have to assess loss forecasting and reserving based on new data sources. Improving bottom lines with AI-powered upsell and cross-sell suggestions also becomes possible. Learn MORE. “and

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats. However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes.

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The DataOps Vendor Landscape, 2021

DataKitchen

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix. Data breaks.

Testing 307
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The new challenges of scale: What it takes to go from PB to EB data scale

CIO Business Intelligence

To accomplish this, we will need additional data center space, more storage disks and nodes, the ability for the software to scale to 1000+PB of data, and increased support through additional compute nodes and networking bandwidth. Focus on scalability.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The approach they’ve used applies to other popular data science APIs such as NumPy , Tensorflow , and so on.

Metadata 105