AWS Big Data

OCTOBER 25, 2023

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. By some estimates, unstructured data can make up to 80–90% of all new enterprise data and is growing many times faster than structured data.

Unstructured Data 129

Unstructured Data Metadata Management Analytics 129

AWS Big Data

JUNE 21, 2023

A typical ask for this data may be to identify sales trends as well as sales growth on a yearly, monthly, or even daily basis. A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. This is achieved by partitioning the data.

Data Warehouse 91

Data Warehouse Data Lake OLAP Cost-Benefit 91

Cloudera

JULY 26, 2021

A common pitfall in the development of data platforms is that they are built around the boundaries of point solutions and are constrained by the technological limitations (e.g., a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g., data warehousing).

Strategy 119

Strategy Data Science Unstructured Data Marketing 119

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

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! Discover why.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

IBM Big Data Hub

APRIL 25, 2023

Similarly, the relational database has been the foundation for data warehousing for as long as data warehousing has been around. Relational databases were adapted to accommodate the demands of new workloads, such as the data engineering tasks associated with structured and semi-structured data, and for building machine learning models.

Optimization 56

Optimization Strategy Data Warehouse Cost-Benefit 56

AWS Big Data

JULY 28, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is designed for analyzing large volumes of data and performing complex queries on structured and semi-structured data. Note that putting a comprehensive data strategy in place is not in scope for this post.

Snapshot 91

Snapshot Metadata Measurement Data Warehouse 91

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly.

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 52

Alation

OCTOBER 21, 2021

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. Many organizations have built a data lake to solve their data storage, access, and utilization challenges.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Jet Global

OCTOBER 1, 2024

With Simba drivers acting as a bridge between Trino and your BI or ETL tools, you can unlock enhanced data connectivity, streamline analytics, and drive real-time decision-making. Let’s explore why this combination is a game-changer for data strategies and how it maximizes the value of Trino and Apache Iceberg for your business.

Dashboards 52

Dashboards Data Lake Reporting Cost-Benefit 52

AWS Big Data

OCTOBER 30, 2024

This is the final part of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to process data with Amazon Redshift Spectrum and create the gold (consumption) layer. The following diagram illustrates the different layers of the data lake.

Data Lake 73

Data Lake Machine Learning Data Architecture Data-driven 73