Smart Data Collective

MARCH 30, 2022

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses. Determine your preparedness.

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Pickl AI

SEPTEMBER 18, 2024

Summary: Netflix’s sophisticated Big Data infrastructure powers its content recommendation engine, personalization, and data-driven decision-making. As a pioneer in the streaming industry, Netflix utilises advanced data analytics to enhance user experience, optimise operations, and drive strategic decisions.

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Smart Data Collective

APRIL 2, 2019

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

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Data Science Dojo

JULY 6, 2023

It integrates seamlessly with other AWS services and supports various data integration and transformation workflows. Google BigQuery: Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics. It provides a scalable and fault-tolerant ecosystem for big data processing.

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Dataconomy

SEPTEMBER 4, 2023

Text analytics is crucial for sentiment analysis, content categorization, and identifying emerging trends. Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data.

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Towards AI

NOVEMBER 15, 2024

Now you can see the Data storage option. I’m using Containers to store data as this supports large amounts of data and can be used for Data Lakes and big data analytics. In that, you choose any as per your requirement. Click on the Plus container to create a container.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

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DagsHub

NOVEMBER 11, 2024

These processes are essential in AI-based big data analytics and decision-making. Data Lakes Data lakes are crucial in effectively handling unstructured data for AI applications. Platforms like Azure Data Lake and AWS Lake Formation can facilitate big data and AI processing.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. The data lake environment is required to configure an AWS Glue database table, which is used to publish an asset in the Amazon DataZone catalog.

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IBM Journey to AI blog

MAY 19, 2023

With high-speed file transfer, integrated services and cross-region offerings, IBM Cloud Object Storage allows you to leverage your data securely. In addition, it helps to reduce backup costs, provide permanent access to archived data, store data for cloud-native applications and create data lakes for big data analytics and AI.

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Smart Data Collective

DECEMBER 13, 2022

However, computerization in the digital age creates massive volumes of data, which has resulted in the formation of several industries, all of which rely on data and its ever-increasing relevance. Data analytics and visualization help with many such use cases. It is the time of big data.

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Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. Processing of Data Once the data is stored, Hive provides a metadata layer allowing users to define the schema and create tables.

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Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. Well, let’s find out. Artificial intelligence (AI).

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Flipboard

OCTOBER 25, 2023

The following is a high-level architecture of the solution we can build to process the unstructured data, assuming the input data is being ingested to the raw input object store. The steps of the workflow are as follows: Integrated AI services extract data from the unstructured data.

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AWS Machine Learning Blog

DECEMBER 4, 2023

Esra Kayabalı is a Senior Solutions Architect at AWS, specialized in the analytics domain, including data warehousing, data lakes, big data analytics, batch and real-time data streaming, and data integration. She has worked on commercial, supply chain, and discovery-related projects.

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Towards AI

FEBRUARY 21, 2023

To make this easier, businesses must create an organized data storage and retrieval system. Storage tools like data warehouses and data lakes will help efficiently store the data, streamlining both retrieval and analysis. The analysis helps to identify patterns and trends that can provide actionable insights.

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Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? Data Warehousing concepts and knowledge should be strong.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Esra Kayabalı is a Senior Solutions Architect at AWS, specializing in the analytics domain including data warehousing, data lakes, big data analytics, batch and real-time data streaming and data integration. He loves combining open-source projects with cloud services.

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Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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AWS Machine Learning Blog

MAY 15, 2023

Esra Kayabalı is a Senior Solutions Architect at AWS, specializing in the analytics domain including data warehousing, data lakes, big data analytics, batch and real-time data streaming and data integration. He has worked on Personalization and Supply Chain related projects.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

The following diagram shows two different data scientist teams, from two different AWS accounts, who share and use the same central feature store to select the best features needed to build their ML models. Cross-account feature group controls With SageMaker Feature Store, you can share feature group resources across accounts.

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Precisely

OCTOBER 16, 2023

Rapid advancements in digital technologies are transforming cloud-based computing and cloud analytics. Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage.

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