Remove Data Analytics Remove Data Processing Remove Data Science 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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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

With the rapid growth of technology, more and more data volume is coming in many different formats—structured, semi-structured, and unstructured. Data analytics on operational data at near-real time is becoming a common need.

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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Amaterasu — is a deployment tool for data pipelines.

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

CIO Business Intelligence

How is it possible to manage the data lifecycle, especially for extremely large volumes of unstructured data? Unlike structured data, which is organized into predefined fields and tables, unstructured data does not have a well-defined schema or structure.

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Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker

AWS Big Data

It includes massive amounts of unstructured data in multiple languages, starting from 2008 and reaching the petabyte level. In the training of GPT-3, the Common Crawl dataset accounts for 60% of its training data, as shown in the following diagram (source: Language Models are Few-Shot Learners ). It is continuously updated.

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Dancing with Elephants in 5 Easy Steps

Cloudera

Perhaps one of the most significant contributions in data technology advancement has been the advent of “Big Data” platforms. Historically these highly specialized platforms were deployed on-prem in private data centers to ensure greater control , security, and compliance. Streaming data analytics. .

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

Over the past 5 years, big data and BI became more than just data science buzzwords. Without real-time insight into their data, businesses remain reactive, miss strategic growth opportunities, lose their competitive edge, fail to take advantage of cost savings options, don’t ensure customer satisfaction… the list goes on.