IBM Big Data Hub

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

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 72

Data Lake Data Governance Data Warehouse Data-driven 72

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

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 78

Analytics Data Warehouse Data Lake Metadata 78

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

IBM Big Data Hub

OCTOBER 16, 2023

ML also helps businesses forecast and decrease customer churn (the rate at which a company loses customers), a widespread use of big data. Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Alation

APRIL 14, 2022

A cloud environment with such features will support collaboration across departments and across common data types, including csv, JSON, XML, AVRO, Parquet, Hyper, TDE, and more. It’s More Important to Know What Your Data Means Than Where It Is. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

Smart Data Collective

AUGUST 7, 2019

Big data has the power to transform any small business. One study found that 77% of small businesses don’t even have a big data strategy. If your company lacks a big data strategy, then you need to start developing one today. Using Big Data to Fix Your Biggest Problems as a Business Owner.

Data Analytics 111

Data Analytics Analytics Big Data Advertising 111

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

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 108

Data Lake Data Warehouse Data-driven Big Data 108