Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. After a few iterations, this results in a well-defined business question with identifiable supporting data.

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

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

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

Cloudera

SEPTEMBER 12, 2018

It was deeply gratifying to see so many organizations deploying the tools and techniques of data science and advanced analytics to solve difficult and important problems. I predict that next year’s competition will be even more amazing as we continue pushing the frontiers of data science forward. Societal Impact:

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

AWS Big Data

MAY 16, 2024

As an AWS Partner, CARTO offers a software solution on the curated digital catalog AWS Marketplace that seamlessly integrates distinctive capabilities for spatial visualization, analysis, and app development directly within the AWS data warehouse environment. To learn more, visit CARTO.

Data Warehouse 108

Data Warehouse Visualization Cost-Benefit Optimization 108

Domino Data Lab

APRIL 3, 2019

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.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

O'Reilly on Data

OCTOBER 19, 2021

This introduces further requirements: The scale of operations is often two orders of magnitude larger than in the earlier data-centric environments. Not only is data larger, but models—deep learning models in particular—are much larger than before. Adapted from the book Effective Data Science Infrastructure.

IT 347

IT Testing Experimentation Software 347

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Learn more at [link]. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

IBM Big Data Hub

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs.

Data Warehouse 107

Data Warehouse Machine Learning Cost-Benefit Metadata 107

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Scale the problem to handle complex data structures. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Sanjeev Mohan

SEPTEMBER 14, 2020

The boundaries between data management and advanced analytics are blurring fast. Databases are enhancing capabilities to build, train and validate machine learning models right where the data sits – inside the databases and data warehouses. to sentiment analysis to graph and time series analysis.

Machine Learning 93

Machine Learning Data Warehouse Deep Learning Statistics 93

IBM Big Data Hub

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. Fortunately, data stores serve as secure data repositories and enable foundation models to scale in both terms of their size and their training data.

Modeling 56

Modeling Cost-Benefit Data Lake Machine Learning 56

Cloudera

MAY 22, 2018

They want to bring a rich variety of tools to bear, whatever fits the need of the moment, be it simple query or search or advanced data science or real-time streaming analytics. If it is too hard to do what they need to do, they’ll abandon the task, and nothing is learned. They want fast and easy self-service.

Enterprise 40

Enterprise Recreation/Entertainment Data Warehouse Machine Learning 40

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

CIO Business Intelligence

JANUARY 20, 2023

A data lakehouse supports business intelligence (BI), analytics, real-time data applications, data science and ML in one place. It provides rapid, direct access to trusted data for data scientists, business analysts, and others who need data to drive business value. Just starting out with analytics?

Analytics 137

Analytics Key Performance Indicator Unstructured Data IoT 137

Domino Data Lab

OCTOBER 14, 2020

We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. Our scientists and teams are familiar with working in Spark on EMR and using data with our existing feature store and data warehouse. TFRecord and Parquet).

Experimentation 99

Experimentation Modeling Deep Learning Testing 99