AWS Big Data

JUNE 28, 2023

QuickSight makes it incredibly simple and intuitive to get to answers with Amazon QuickSight Q , which allows users to ask business questions about their data in natural language and receive answers quickly through data visualizations. Improving the zero-ETL performance is a continuous goal for AWS.

Analytics 69

Analytics Data Warehouse Data Lake Data-driven 69

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning.

Data Quality 95

Data Quality Statistics Data Lake Visualization 95

BizAcuity

MAY 16, 2022

So, make sure you have a data strategy in place. Data Integration. The easiest way to tap into data is integrating all your data to get a detailed understanding of your operations and your customers. Data mining. Data mining allows refining and analyzing of the data on a near-real time basis.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

Jet Global

OCTOBER 7, 2020

It uses its own data mart, which cannot be customized in any way. Power BI is an analytical tool for data visualization and discovery. When working with D365 F&SCM data, it typically requires specialized programming skills to develop reports or to make changes to existing reports. Enterprise Business Intelligence.

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

Andrew White

OCTOBER 22, 2021

Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. Data Management Infrastructure/Data Fabric 5. Data Integration tactics 4. Enterprise Data Manager 1. Web and Data Services Manager 1. Metadata Strategy 3.

IT 52

IT Data Lake Strategy Data Science 52

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 103

Data Warehouse Data Lake Analytics Machine Learning 103

AWS Big Data

MARCH 27, 2024

AWS has invested in a zero-ETL (extract, transform, and load) future so that builders can focus more on creating value from data, instead of having to spend time preparing data for analysis. You can send data from your streaming source to this resource for ingesting the data into a Redshift data warehouse.

Data Analytics 94

Data Analytics Analytics Data Warehouse Data Lake 94

AWS Big Data

FEBRUARY 13, 2024

AWS Glue has made this more straightforward with the launch of AWS Glue job observability metrics , which provide valuable insights into your data integration pipelines built on AWS Glue. With Grafana, you can create, explore, and share visually rich, data-driven dashboards. Graph lines are visualized per job.

Metrics 101

Metrics Dashboards Visualization Key Performance Indicator 101

AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. Choose Save. Choose Register location.

Data Lake 95

Data Lake Snapshot Metadata Optimization 95

AWS Big Data

OCTOBER 27, 2023

Data integration is the foundation of robust data analytics. It encompasses the discovery, preparation, and composition of data from diverse sources. In the modern data landscape, accessing, integrating, and transforming data from diverse sources is a vital process for data-driven decision-making.

Analytics 99

Analytics Visualization Data Integration Cost-Benefit 99

AWS Big Data

APRIL 20, 2023

In a modern data architecture, unified analytics enable you to access the data you need, whether it’s stored in a data lake or a data warehouse. AWS Glue provides an extensible architecture that enables users with different data processing use cases, and works well with Amazon Redshift.

Visualization 79

Visualization Data Warehouse Big Data Data Lake 79

AWS Big Data

NOVEMBER 29, 2023

With Amazon Bedrock , you can privately customize FMs for your specific use case using a small set of your own labeled data through a visual interface without writing any code. You also need services to store data for analysis and machine learning (ML) like Amazon Simple Storage Service (Amazon S3).

Data Warehouse 111

Data Warehouse Data Lake Data Integration Dashboards 111

Ontotext

MARCH 8, 2023

As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

Data Warehouse 130

Data Warehouse Data Lake Visualization Big Data 130

AWS Big Data

FEBRUARY 1, 2023

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. SQL Server Integration Services (SSIS): You know it; your father used it.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

IBM Big Data Hub

APRIL 27, 2022

The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. Read: The first capability of a data fabric is a semantic knowledge data catalog, but what are the other 5 core capabilities of a data fabric? 11 May 2021. .

Management 86

Management Metadata Data Architecture Data Lake 86

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

AWS Big Data

JULY 19, 2023

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model.

Modeling 69

Modeling Sales Data Warehouse Snapshot 69

Domino Data Lab

APRIL 3, 2019

The longer answer is that in the context of machine learning use cases, strong assumptions about data integrity lead to brittle solutions overall. Most of the data management moved to back-end servers, e.g., databases. So we had three tiers providing a separation of concerns: presentation, logic, data.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

JUNE 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 76

Data Quality Data Lake Data-driven Metrics 76

Sisense

JULY 22, 2020

The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3 , Hadoop and Microsoft Azure. Sisense provides instant access to your cloud data warehouses.

Slice and Dice 98

Slice and Dice Digital Transformation Data Warehouse Data Lake 98

CIO Business Intelligence

SEPTEMBER 22, 2023

All this data arrives by the terabyte, and a data management platform can help marketers make sense of it all. DMPs excel at negotiating with a wide array of databases, data lakes, or data warehouses, ingesting their streams of data and then cleaning, sorting, and unifying the information therein.

Management 105

Management Advertising Data Lake Sales 105

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

CIO Business Intelligence

JUNE 9, 2022

All this data arrives by the terabyte, and a data management platform can help marketers make sense of it all. Marketing-focused or not, DMPs excel at negotiating with a wide array of databases, data lakes, or data warehouses, ingesting their streams of data and then cleaning, sorting, and unifying the information therein.

Management 144

Management Advertising Data Lake Sales 144

BizAcuity

AUGUST 1, 2022

TIBCO Jaspersoft offers a complete BI suite that includes reporting, online analytical processing (OLAP), visual analytics , and data integration. The web-scale platform enables users to share interactive dashboards and data from a single page with individuals across the enterprise. Good Visualization Options.

Reporting 52

Reporting OLAP Online Analytical Processing Dashboards 52

IBM Big Data Hub

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

Cost-Benefit 82

Cost-Benefit Metadata Data Governance Modeling 82

Cloudera

MAY 16, 2018

All three cases require a “big picture” approach that incorporates new and alternative data sources and cross-functional collaboration throughout the organization not only to identify illegal activities, rogue traders, or personal misconduct but also to provide evidential material that demonstrates a deep understanding of the intent.

Data Lake 41

Data Lake Risk Visualization Unstructured Data 41

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).

Data Processing 89

Data Processing Data Warehouse Enterprise Visualization 89

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

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

APRIL 26, 2022

Jet Reports now offers high performance connectivity with options to connect to Synapse/Azure Data Lakes, BYOD, SQL or your Cubes and Tabular models. Create accurate charts, graphs, and other visuals that combine the most current data from multiple applications in just a few clicks. With the release of Jet Reports 22.1,

Reporting 52

Reporting Finance Cost-Benefit Forecasting 52