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.

Data Lakes 78

Data Lakes Big Data Analytics Big Data Analytics Cloud Computing 78

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.

AI 91

AI AI Artificial Intelligence Artificial Intelligence 91

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).

AWS 114

AWS Cloud Computing Data Lakes Database 114

Pickl AI

APRIL 6, 2023

An example of the Azure Data Engineer Jobs in India can be evaluated as follows: 6-8 years of experience in the IT sector. Data Warehousing concepts and knowledge should be strong. Having experience using at least one end-to-end Azure data lake project. Knowledge in using Azure Data Factory Volume.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

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.

AWS 97

AWS Algorithm Machine Learning Machine Learning 97

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.

AWS 103

AWS ML ML Machine Learning 103

Pickl AI

JULY 6, 2023

Let’s understand the key stages in the data flow process: Data Ingestion Data is fed into Hadoop’s distributed file system (HDFS) or other storage systems supported by Hive, such as Amazon S3 or Azure Data Lake Storage.

Hadoop 52

Hadoop SQL Big Data Big Data 52

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.

AWS 83

AWS Data Lakes Clustering Data Preparation 83

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.

AWS 93

AWS ML ML Predictive Analytics 93

Smart Data Collective

DECEMBER 13, 2022

There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Data lakes hold raw data that has not yet been altered to meet a specific purpose.

Data Analysis 134

Data Analysis Data Analysis Analytics Analytics 134

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.

AWS 132

AWS ML ML Analytics 132

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.

Algorithm 68

Algorithm Data Scientist AWS Machine Learning 68

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.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72