CIO Business Intelligence

JUNE 20, 2022

Otis One’s cloud-native platform is built on Microsoft Azure and taps into a Snowflake data lake. IoT sensors send elevator data to the cloud platform, where analytics are applied to support business operations, including reporting, data visualization, and predictive modeling.

Internet of Things 105

Internet of Things IoT Manufacturing Machine Learning 105

Cloudera

NOVEMBER 18, 2020

The Advanced Analytics team supporting the businesses of Merck KGaA, Darmstadt, Germany was able to establish a data governance framework within its enterprise data lake. This enabled Merck KGaA to control and maintain secure data access, and greatly increase business agility for multiple users.

Internet Publishing and Broadcasting 123

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 123

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 86

Analytics Data Warehouse Data Lake Metadata 86

CIO Business Intelligence

DECEMBER 13, 2022

We’re looking at a variety of sources of data, putting it in data lakes, and then using that to drive predictive models that really help our doctors and our care teams to stratify our patient’s risk by taking actions at the right time.

Strategy 98

Strategy Technology Digital Transformation Data Lake 98

Darkhorse

JULY 6, 2017

So what is data wrangling? Let’s imagine the process of building a data lake. Let’s further pretend you’re starting out with the aim of doing a big predictive modeling thing using machine learning. First off, data wrangling is gathering the appropriate data. Can you start modelling now?

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Domino Data Lab

JANUARY 11, 2021

Writing data from Domino into Snowflake. Once a model has been developed, the model needs to be productionized either via an app, an API or in this case, writing model scores from the prediction model back into Snowflake so that business analyst end users are able to access predictions via their reporting tools.

Recreation/Entertainment 103

Recreation/Entertainment Data Science Data Warehouse Publishing 103

Cloudera

MAY 12, 2021

Data Security & Governance: Merck KGaA, Darmstadt, Germany — Established a data governance framework with their data lake to discover, analyze, store, mine, and govern relevant data. Industry Transformation: Telkomsel — Ingesting 25TB of data daily to provide advanced customer analytics in real-time .

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

IBM Big Data Hub

SEPTEMBER 19, 2023

This iterative process is known as the data science lifecycle, which usually follows seven phases: Identifying an opportunity or problem Data mining (extracting relevant data from large datasets) Data cleaning (removing duplicates, correcting errors, etc.) Watsonx comprises of three powerful components: the watsonx.ai

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

BizAcuity

JULY 13, 2021

It can reduce the whole process of transforming data to information to action in a matter of days and weeks instead of months with a unique Pay-As-You-Go licensing model that allows clients to get started with very minimal capital & operational cost. Data Enrichment/Data Warehouse Layer. Data Analytics Layer.

Optimization 52

Optimization Marketing Analytics Data Warehouse 52

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Cloudera

JULY 18, 2018

Now organizations can reap all the benefits of having an enterprise data lake, in addition to an advanced analytics solution enabling them to put machine learning and AI into action at massive scale to improve health outcomes for individuals and entire populations alike.

Machine Learning 51

Machine Learning Predictive Analytics Analytics Prescriptive Analytics 51

IBM Big Data Hub

OCTOBER 16, 2023

Foundation models can use language, vision and more to affect the real world. GPT-3, OpenAI’s language prediction model that can process and generate human-like text, is an example of a foundation model. They are used in everything from robotics to tools that reason and interact with humans.

Risk 70

Risk Modeling Management Metadata 70

AWS Big Data

NOVEMBER 14, 2023

Ten years ago, we launched Amazon Kinesis Data Streams , the first cloud-native serverless streaming data service, to serve as the backbone for companies, to move data across system boundaries, breaking data silos. Another integration launched in 2023 is with Amazon Monitron to power predictive maintenance management.

IoT 59

IoT Data-driven Data Lake Data Strategy 59

Alation

APRIL 14, 2022

For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

IBM Big Data Hub

OCTOBER 16, 2023

Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation. Banks and other lenders use ML classification algorithms and predictive models to determine who they will offer loans to. Many stock market transactions use ML.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

AWS Big Data

JULY 28, 2023

Amazon Redshift now makes it easier for you to run queries in AWS data lakes by automatically mounting the AWS Glue Data Catalog. You no longer have to create an external schema in Amazon Redshift to use the data lake tables cataloged in the Data Catalog.

Data Lake 81

Data Lake Data Governance Data Warehouse Modeling 81

Cloudera

MAY 23, 2024

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes. This makes gathering information for decision making a challenge.

Management 80

Management Metrics Data Processing Data Lake 80

AWS Big Data

DECEMBER 19, 2024

Delta tables technical metadata is stored in the Data Catalog, which is a native source for creating assets in the Amazon DataZone business catalog. Access control is enforced using AWS Lake Formation , which manages fine-grained access control and data sharing on data lake data.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

Cloudera

MAY 30, 2024

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission-critical, large-scale data analytics and AI use cases—including enterprise data warehouses. Support for Modern Analytics Workloads : With support for both SQL-based querying and advanced analytics frameworks (e.g.,

Data Lake 74

Data Lake Analytics Data Warehouse Machine Learning 74

AWS Big Data

OCTOBER 23, 2024

Amazon Redshift enables data warehousing by seamlessly integrating with other data stores and services in the modern data organization through features such as Zero-ETL , data sharing , streaming ingestion , data lake integration , and Redshift ML.

Data Warehouse 104

Data Warehouse Cost-Benefit Data Lake Data-driven 104

AWS Big Data

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Big Data Architect.

Data Lake 117

Data Lake Data Warehouse Data-driven Big Data 117

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