Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ). Forrester ). Gartner ).

Data Architecture 79

Data Architecture Data Lake Data Warehouse Metadata 79

erwin

JULY 17, 2019

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

CIO Business Intelligence

SEPTEMBER 8, 2022

Most organizations understand the profound impact that data is having on modern business. In Foundry’s 2022 Data & Analytics Study , 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years.

Cost-Benefit 122

Cost-Benefit Internet of Things Data-driven Data Lake 122

CIO Business Intelligence

OCTOBER 2, 2024

With different people filtering and augmenting data, you need to trace who makes which changes and why, and you need to know which version of the data set was used to train a given model. And with all the data an enterprise has to manage, it’s essential to automate the processes of data collection, filtering, and categorization.

Management 141

Management Data Governance Cost-Benefit Structured Data 141

AWS Big Data

DECEMBER 5, 2023

The counties that are in lighter shades represent limited survey responses and need to be included in the targeted data collection strategy. Finally, the dashboard’s user-friendly interface made survey data more accessible to a wider range of stakeholders. The first image shows the dashboard without any active filters.

Measurement 94

Measurement Dashboards Data Warehouse Analytics 94

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model? What is a data vault?

Data Warehouse 71

Data Warehouse Data Lake Cost-Benefit Metadata 71

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

Cloudera

DECEMBER 16, 2022

Cloudera has long had the capabilities of a data lakehouse, if not the label. Cloudera enables an open data lakehouse architecture that combines all the flexibility of the data lake with the performance of the data warehouse, so enterprises can use all data — both structured and unstructured.

Management 92

Management Metadata Machine Learning Data Lake 92

Alation

MAY 16, 2023

Sources can include analytics data regarding user behavior, transactional data from ecommerce websites, and third-party data from other organizations. It’s worth noting that a data pipeline may have more than one data source. Ingestion tools are connected to various data sources.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. CRM platforms). CRM platforms).

Data Processing 94

Data Processing Data Warehouse Enterprise Visualization 94

Sisense

APRIL 26, 2020

In our modern digital world, proper use of data can play a huge role in a business’s success. Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Cloudera

FEBRUARY 16, 2022

Each workspace is associated with a collection of cloud resources. 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. The highest level construct in CML is a workspace.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Alation

MAY 16, 2023

Sources can include analytics data regarding user behavior, transactional data from ecommerce websites, and third-party data from other organizations. It’s worth noting that a data pipeline may have more than one data source. Ingestion tools are connected to various data sources.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Ontotext

NOVEMBER 16, 2023

In a data mesh, domains are represented by a node, which can be an operational data store (ODS), a data warehouse, or a data lake tailored to the domain’s requirements. Don’t Forget Team Structure Team and organizational structure are an important aspect to consider for data mesh.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Alation

JANUARY 26, 2023

Below are some examples of common data governance goals: All data collection, storage, and usage must meet the terms of legislation. Avoid fines that could result from issues such as data leakage or lack of data minimization practices. This is “table stakes” for any data governance program!).

Data Governance 52

Data Governance Strategy Data Quality Data Collection 52

Alation

FEBRUARY 13, 2020

More often than not, today, the key to unlocking that accomplishment sits within a tsunami of data: data collected from consumers, applications, and sensors. Each of your organizations has something impossible to accomplish. I don’t know exactly what it is, but I know it’s there.

Machine Learning 52

Machine Learning Data-driven Big Data Technology 52

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

datapine

AUGUST 29, 2022

The new edition also explores artificial intelligence in more detail, covering topics such as Data Lakes and Data Sharing practices. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz.

Big Data 263

Big Data Data Analytics Analytics Data mining 263