Remove Data Lake Remove Data Science Remove Publishing Remove Visualization
article thumbnail

A Detailed Introduction on Data Lakes and Delta Lakes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A data lake is a central data repository that allows us to store all of our structured and unstructured data on a large scale.

Data Lake 257
article thumbnail

Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Data analytics on operational data at near-real time is becoming a common need. Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. For more information, see Changing the default settings for your data lake.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Achieving Trusted AI in Manufacturing

Cloudera

Here are some of the key use cases: Predictive maintenance: With time series data (sensor data) coming from the equipment, historical maintenance logs, and other contextual data, you can predict how the equipment will behave and when the equipment or a component will fail. Eliminate data silos.

article thumbnail

How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

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.

article thumbnail

Week in the Life of an Analyst at Gartner US IT Symposium (virtual) 2021

Andrew White

Analytics Tactics (known outcome/known data/BI/analytics v unknown outcome/unknown data/data science/ML) 11. Data Hub Strategy 10. Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. VP Data and Analytics 1.

IT 52
article thumbnail

Data for All: Empowering Users With AI, ML, and Analytics

Sisense

Last year, Sisense released both Sisense Narratives , which creates descriptive text to help end users understand their visualizations as well as Sisense Insight Miner , which looks for unusual relationships in the data and surfaces them for further analysis. The hardware is there, the data is accumulating fast. Why AI Now?

article thumbnail

Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.