AWS Big Data

JANUARY 8, 2024

In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices. Stream ingestion – The stream ingestion layer is responsible for ingesting data into the stream storage layer.

Analytics 109

Analytics IoT Data-driven Snapshot 109

CIO Business Intelligence

JUNE 29, 2022

Azure Data Explorer is used to store and query data in services such as Microsoft Purview, Microsoft Defender for Endpoint, Microsoft Sentinel, and Log Analytics in Azure Monitor. Azure Data Lake Analytics. Data warehouses are designed for questions you already know you want to ask about your data, again and again.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

CIO Business Intelligence

MAY 24, 2022

However, more than 99 percent of respondents said they would migrate data to the cloud over the next two years. The Internet of Things (IoT) is a huge contributor of data to this growing volume, iotaComm estimates there are 35 billion IoT devices worldwide and that in 2025 all IoT devices combined will generate 79.4

Data-driven 98

Data-driven Data Lake Data Warehouse Cost-Benefit 98

AWS Big Data

FEBRUARY 21, 2023

At the heart of all data warehousing is integration, and this layer contains integrated data from multiple sources built around the enterprise-wide business keys. Although data lakes resemble data vaults, a data vault provides more features of a data warehouse. What is a hybrid model?

Data Warehouse 63

Data Warehouse Data Lake Cost-Benefit Modeling 63

Sisense

MAY 11, 2021

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 126

Metadata Cost-Benefit Enterprise Interactive 126

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52