Birst BI

FEBRUARY 25, 2020

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead. agentless) Birst to Snowflake real-time connector. What gaps does the joint solution address in the market?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Architecture Enterprise 52

Data Virtualization

OCTOBER 5, 2022

Reading Time: 3 minutes At the heart of every organization lies a data architecture, determining how data is accessed, organized, and used. For this reason, organizations must periodically revisit their data architectures, to ensure that they are aligned with current business goals.

Data Lake 52

Data Lake Data Architecture Data Integration Management 52

CIO Business Intelligence

OCTOBER 13, 2023

Data architecture is a complex and varied field and different organizations and industries have unique needs when it comes to their data architects. Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes.

Data Architecture 131

Data Architecture Data Warehouse Statistics Visualization 131

TDAN

JUNE 16, 2020

When companies embark on a journey of becoming data-driven, usually, this goes hand in and with using new technologies and concepts such as AI and data lakes or Hadoop and IoT. Suddenly, the data warehouse team and their software are not the only ones anymore that turn data […].

Data Warehouse 52

Data Warehouse Data-driven Data Lake IoT 52

CDW Research Hub

JULY 18, 2022

While many organizations understand the business need for a data and analytics cloud platform , few can quickly modernize their legacy data warehouse due to a lack of skills, resources, and data literacy. Cost reduction and best business practices. Overall data architecture and strategy.

Optimization 87

Optimization Data Lake Data Warehouse Manufacturing 87

AWS Big Data

NOVEMBER 14, 2023

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it straightforward and cost-effective to analyze all your data at petabyte scale, using standard SQL and your existing business intelligence (BI) tools. Today, tens of thousands of customers run business-critical workloads on Amazon Redshift.

Data Warehouse 59

Data Warehouse Analytics Testing Cost-Benefit 59

CIO Business Intelligence

JUNE 23, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 90

Data Lake Data Warehouse Machine Learning Cost-Benefit 90

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 98

Data Warehouse Data Lake Analytics Machine Learning 98

AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 86

Data Lake Data Warehouse Data-driven B2B 86

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 93

Data Lake Data Warehouse Machine Learning Cost-Benefit 93

AWS Big Data

FEBRUARY 1, 2024

In this post, we provide a solution architecture that describes how you can process data from three different types of sources—streaming, transactional, and third-party reference data—and aggregate them in Amazon Redshift for business intelligence (BI) reporting. version cluster. version cluster.

Data Warehouse 120

Data Warehouse Dashboards Risk Management Risk 120

Jen Stirrup

MARCH 29, 2023

Organisations are looking at ways of simplifying data; for example, through simple rebranding efforts to disguise the complexity. However, SAP Datasphere goes much deeper deeper than a simple rebranding; it is the next generation of SAP Data Warehouse Cloud. Business Intelligence is often a search problem in disguise.

Metadata 120

Metadata Data Warehouse Data Integration Business Intelligence 120

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. These upstream data sources constitute the data producer components.

Data Warehouse 89

Data Warehouse Data Lake Analytics Data Science 89

Smarten

MAY 30, 2023

When a business plans to launch an augmented analytics or business intelligence solution, it must carefully plan for user adoption – especially if the launch of this solution is part of a larger strategy for self-serve business user analytics and the Citizen Data Scientist approach to data use across the enterprise.

Slice and Dice 52

Slice and Dice Business Intelligence Cost-Benefit Dashboards 52

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift is available in both serverless and provisioned configurations.

Data Lake 90

Data Lake Data Warehouse Metadata Data Architecture 90

CIO Business Intelligence

SEPTEMBER 14, 2023

Database-centric: In larger organizations, where managing the flow of data is a full-time job, data engineers focus on analytics databases. Database-centric data engineers work with data warehouses across multiple databases and are responsible for developing table schemas.

Analytics 134

Analytics Data Science Unstructured Data Data mining 134

CIO Business Intelligence

NOVEMBER 28, 2023

But at the other end of the attention spectrum is data management, which all too frequently is perceived as being boring, tedious, the work of clerks and admins, and ridiculously expensive. Still, to truly create lasting value with data, organizations must develop data management mastery.

Management 115

Management Data Architecture Data Lake Data Strategy 115

CDW Research Hub

JULY 27, 2021

Data volumes are growing exponentially, and traditional, on-premises data warehouses are constrained, overly complex, and costly to scale. Companies feel locked into these systems as they are often tied to business-critical operations. Reduce the total cost of ownership of the data infrastructure.

Data Warehouse 52

Data Warehouse Data Lake Data Architecture Consulting 52

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. The decoupled compute and storage architecture of Amazon Redshift enables you to build highly scalable, resilient, and cost-effective workloads.

Analytics 93

Analytics Data Warehouse Testing Dashboards 93

AWS Big Data

NOVEMBER 9, 2023

The aim was to bolster their analytical capabilities and improve data accessibility while ensuring a quick time to market and high data quality, all with low total cost of ownership (TCO) and no need for additional tools or licenses. It’s raw, unprocessed data straight from the source.

Data Warehouse 101

Data Warehouse Testing Data Quality Reporting 101

AWS Big Data

NOVEMBER 13, 2023

11:30 AM – 12:30 PM (PDT) Ceasars Forum ANT318 | Accelerate innovation with end-to-end serverless data architecture. 4:30 PM – 5:30 PM (PDT) Wynn ANT207 | Understand your data with business context. 1:00 PM – 2:00 PM (PDT) Venetian ANT201 | Accelerate innovation with real-time data.

Analytics 109

Analytics Data Lake Data Warehouse Data-driven 109

AWS Big Data

SEPTEMBER 21, 2023

Satori integrates natively with both Amazon Redshift provisioned clusters and Amazon Redshift Serverless for easy setup of your Amazon Redshift data warehouse in the secure Satori portal. In part 2, we will explore how to set up self-service data access with Satori to data stored in Amazon Redshift.

Data Warehouse 82

Data Warehouse Interactive Data Architecture Data Lake 82

Smarten

JUNE 1, 2022

An integrated solution provides single sign-on access to data sources and data warehouses.’. This is an expensive and time-consuming process and one that will require you to constantly update skills and the solution to keep pace with the market and with technology. Rapid Deployment.

Data Warehouse 76

Data Warehouse Software Data Architecture Consulting 76

Cloudera

DECEMBER 19, 2018

Growth factors and business priority are ever changing. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. Natural language analytics and streaming data analytics are emerging technologies that will impact the market.

Data Warehouse 52

Data Warehouse Analytics Big Data Data Architecture 52

CIO Business Intelligence

MARCH 26, 2024

Cao shared that Huawei’s data intelligence solution combines an all-serverless architecture with data lakehouse and data-AI convergence. Using transaction data in real time for analysis and decisions is all about the real-time data capability,” said Mr.

Strategy 59

Strategy Uncertainty Finance Digital Transformation 59

IBM Big Data Hub

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. Watsonx comprises of three powerful components: the watsonx.ai

Data Science 81

Data Science Data Analytics Prescriptive Analytics Analytics 81

CIO Business Intelligence

APRIL 25, 2022

The other 10% represents the effort of initial deployment, data-loading, configuration and the setup of administrative tasks and analysis that is specific to the customer, the Henschen said. Partner solutions to boost functionality, adoption.

Recreation/Entertainment 115

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 115

AWS Big Data

MARCH 10, 2023

However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes. They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. Dimension-based models have been used extensively to build data warehouses.

Data Lake 99

Data Lake Sales Data Warehouse Snapshot 99

CIO Business Intelligence

JUNE 24, 2022

Your data architecture should also include the common data models of VSM tools to yield a holistic view,” Higgins said. Some of the other approaches include prioritizing cloud platforms to change technical architecture in a way to support business innovation, and embracing cloud-native to accelerate cloud modernization.

Modeling 101

Modeling Digital Transformation Data Architecture Data-driven 101

Ontotext

AUGUST 4, 2023

A data fabric utilizes an integrated data layer over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of data across enterprises, including hybrid and multi-cloud platforms. Data fabric does not replace data warehouses, data lakes, or data lakehouses.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

CIO Business Intelligence

APRIL 12, 2023

After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It isn’t easy.

Machine Learning 138

Machine Learning Cost-Benefit Data-driven Strategy 138

Data Virtualization

JANUARY 19, 2023

Reading Time: 2 minutes Today, many businesses are modernizing their on-premises data warehouses or cloud-based data lakes using Microsoft Azure Synapse Analytics. Unfortunately, with data spread.

Data Analytics 52

Data Analytics Data Lake Data Warehouse Analytics 52

CIO Business Intelligence

JUNE 23, 2023

There are also no-code data engineering and AI/ML platforms so regular business users, as well as data engineers, scientists and DevOps staff, can rapidly develop, deploy, and derive business value.

Data Strategy 111

Data Strategy Strategy Visualization Consulting 111

Octopai

JUNE 15, 2020

Business intelligence databases are dynamic repositories that must often be adjusted based on organizational and or regulatory requirements. With the insurance company’s current data architecture, the process would have no chance of being completed in time for the change. How can BI teams reduce this laborious process?

Insurance 52

Insurance Metadata Business Intelligence Data Warehouse 52

TDAN

MAY 31, 2022

As we enter a new cloud-first era, advancements in technology have helped companies capture and capitalize on data as much as possible. Deciding between which cloud architecture to use has always been a debate between two options: data warehouses and data lakes.

Data Lake 52

Data Lake Data Warehouse Technology Data Architecture 52

Jedox

OCTOBER 22, 2020

Too often, Finance & FP&A has been constrained by the data architecture our organizations have built (or maybe not built) over the past twenty or so years. Typically, we take our multiple data sources and perform some level of ETL on the data.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

CIO Business Intelligence

APRIL 4, 2023

In order to move AI forward, we need to first build and fortify the foundational layer: data architecture. This architecture is important because, to reap the full benefits of AI, it must be built to scale across an enterprise versus individual AI applications. Constructing the right data architecture cannot be bypassed.

Data Architecture 52

Data Architecture Data Lake Data Warehouse Cost-Benefit 52