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. Optimizing Snowflake functionality. Overall data architecture and strategy. Workload discovery.

Optimization 87

Optimization Data Lake Data Warehouse Manufacturing 87

AWS Big Data

APRIL 29, 2024

The construction of big data applications based on open source software has become increasingly uncomplicated since the advent of projects like Data on EKS , an open source project from AWS to provide blueprints for building data and machine learning (ML) applications on Amazon Elastic Kubernetes Service (Amazon EKS).

Management 98

Management Big Data Metadata Data Warehouse 98

Sisense

MARCH 14, 2021

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. Big data challenges and solutions.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

AWS Big Data

MAY 2, 2024

Traditionally, they had to use QE v2 for their development tasks, which wasn’t the most optimal solution. However, this new feature resolves that issue by enabling data engineers and developers to seamlessly integrate their development work within VS Code, enhancing their workflow efficiency. Virginia)).

Visualization 95

Visualization Data Warehouse Data-driven Management 95

Cloudera

SEPTEMBER 10, 2018

Among the many reasons that a majority of large enterprises have adopted Cloudera Data Warehouse as their modern analytic platform of choice is the incredible ecosystem of partners that have emerged over recent years. Informatica’s Big Data Manager and Qlik’s acquisition of Podium Data are just 2 examples.

Data Warehouse 44

Data Warehouse Unstructured Data Internet of Things Enterprise 44

CIO Business Intelligence

OCTOBER 13, 2023

Data architect Armando Vázquez identifies eight common types of data architects: Enterprise data architect: These data architects oversee an organization’s overall data architecture, defining data architecture strategy and designing and implementing architectures.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

Smart Data Collective

JUNE 7, 2019

This is where real-time stream processing enters the picture, and it may probably change everything you know about big data. Read this article as we’ll tackle what big data and stream processing are. We’ll also deal with how big data stream processing can help new emerging markets in the world.

Big Data 81

Big Data Marketing Cost-Benefit Unstructured Data 81

IBM Big Data Hub

OCTOBER 1, 2018

It also runs on market-leading IBM Big Data Servers and IBM FlashSystem 900 storage arrays enabling a high-performance system optimized for business insight as well as advanced operational and in-database analytics.

Data Warehouse 72

Data Warehouse Machine Learning Big Data Optimization 72

AWS Big Data

APRIL 10, 2024

When data is used to improve customer experiences and drive innovation, it can lead to business growth,” – Swami Sivasubramanian , VP of Database, Analytics, and Machine Learning at AWS in With a zero-ETL approach, AWS is helping builders realize near-real-time analytics. Choose a suitable instance size (the default is db.r5.2xlarge ).

Data Warehouse 95

Data Warehouse Analytics Metrics Snapshot 95

AWS Big Data

NOVEMBER 17, 2023

In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. In an optimal environment, we store the credentials in AWS Secrets Manager and retrieve them. Mohamed Hamdy is a Senior Big Data Architect with AWS Professional Services based in London, UK.

Snapshot 92

Snapshot Data Processing Testing Data Warehouse 92

AWS Big Data

FEBRUARY 1, 2024

Trade quality and optimization – In order to monitor and optimize trade quality, you need to continually evaluate market characteristics such as volume, direction, market depth, fill rate, and other benchmarks related to the completion of trades. This will be your OLTP data store for transactional data. version cluster.

Data Warehouse 124

Data Warehouse Dashboards Risk Management Risk 124

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 85

Enterprise Data Warehouse Snapshot Cost-Benefit 85

AWS Big Data

NOVEMBER 10, 2023

This integration expands the possibilities for AWS analytics and machine learning (ML) solutions, making the data warehouse accessible to a broader range of applications. Your applications can seamlessly read from and write to your Amazon Redshift data warehouse while maintaining optimal performance and transactional consistency.

Data Processing 94

Data Processing Data Lake Data Warehouse Optimization 94

CIO Business Intelligence

JUNE 23, 2022

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data Lake 98

Data Lake Data Warehouse Machine Learning Cost-Benefit 98

Smart Data Collective

JUNE 15, 2021

Fortunately, there are a few quick fixes that you can try to help optimize looker performance and improve dashboard load times. It’s also possible to employ extra caching or materialized views in the data warehouse in addition to caching in Looker (depending on the capability of your data warehouse). Final word.

Data Warehouse 96

Data Warehouse Dashboards Optimization Metrics 96

AWS Big Data

NOVEMBER 28, 2023

As customers become more data driven and use data as a source of competitive advantage, they want to easily run analytics on their data to better understand their core business drivers to grow sales, reduce costs, and optimize their businesses. ETL is the process data engineers use to combine data from different sources.

Data Warehouse 95

Data Warehouse Data-driven Machine Learning B2B 95

Cloudera

JUNE 18, 2022

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data Lake 93

Data Lake Data Warehouse Machine Learning Cost-Benefit 93

CIO Business Intelligence

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 135

Analytics Data Science Unstructured Data Data mining 135

AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. ExampleCorp has multiple Redshift clusters.

Management 97

Management Data Warehouse Metrics Dashboards 97

AWS Big Data

MARCH 29, 2024

Analyzing historical patterns allows you to optimize performance, identify issues proactively, and improve planning. By connecting the new observability metrics to interactive QuickSight dashboards, you can uncover daily, weekly, and monthly patterns to optimize AWS Glue job usage.

Metrics 108

Metrics Visualization Dashboards Interactive 108

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 84

Data Warehouse Cost-Benefit Metadata Data-driven 84

bridgei2i

APRIL 21, 2021

The 2000s saw data warehouses being created and used as business intelligence picked up. In the current landscape, supply chains are an outcome of big data, machine learning and artificial technologies. Arun also illustrates some client use cases and talks about the impact numbers.

Forecasting 98

Forecasting Data Warehouse Manufacturing Machine Learning 98

AWS Big Data

MAY 17, 2023

During that same time, AWS has been focused on helping customers manage their ever-growing volumes of data with tools like Amazon Redshift , the first fully managed, petabyte-scale cloud data warehouse. One group performed extract, transform, and load (ETL) operations to take raw data and make it available for analysis.

Data Warehouse 114

Data Warehouse Cost-Benefit Sales Data-driven 114

AWS Big Data

FEBRUARY 16, 2024

Many customers are extending their data warehouse capabilities to their data lake with Amazon Redshift. They are looking to further enhance their security posture where they can enforce access policies on their data lakes based on Amazon Simple Storage Service (Amazon S3).

Data Lake 100

Data Lake Data Warehouse Testing Business Objectives 100

Cloudera

FEBRUARY 7, 2019

The company sought a data management platform that would allow its enterprise to handle greater data variety, velocity and volume in a cost-effective manner. This has become a core component of the fraud management framework for the business, and has helped solidify its role as the trusted keeper of sensitive data.

Big Data 66

Big Data Data Analytics Analytics Data Architecture 66

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 105

Data Lake Sales Data Warehouse Snapshot 105

IBM Big Data Hub

OCTOBER 30, 2023

IBM and AWS partnership focuses on delivering solutions in areas like: Supply chain optimization with AI-infused Planning Analytics IBM Planning Analytics on AWS offers a powerful platform for supply chain optimization, blending IBM’s analytics expertise with AWS’s cloud capabilities.

Insurance 86

Insurance Data Warehouse Data-driven Unstructured Data 86

Cloudera

DECEMBER 19, 2018

Natural language analytics and streaming data analytics are emerging technologies that will impact the market. Cloud computing has passed the tipping point, with most organizations comfortable moving critical data and applications to the public cloud. Big Data Technologies and Architectures.

Data Warehouse 54

Data Warehouse Analytics Big Data Data Architecture 54

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer job description.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

AWS Big Data

OCTOBER 20, 2023

Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team. He is passionate about architecting fast-growing data environments, diving deep into distributed big data software like Apache Spark, building reusable software artifacts for data lakes, and sharing knowledge in AWS Big Data blog posts.

Data Lake 86

Data Lake Big Data Consulting Data Warehouse 86

AWS Big Data

MARCH 27, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Complete the implementation tasks such as data ingestion and performance testing. Analyze the data and then optimize as necessary.

Testing 101

Testing Data Warehouse Metrics Cost-Benefit 101

IBM Big Data Hub

NOVEMBER 6, 2023

This method requires the enterprise to have clean data flows from central sources of truth to accurately track and reflect usage. Watsonx.data allows enterprises to centrally gather, categorize and filter data from multiple sources. Less than 15% of SMBs want to interact with providers through phone and automated voice systems.

Cost-Benefit 112

Cost-Benefit Enterprise ROI Data Warehouse 112

Smart Data Collective

NOVEMBER 18, 2020

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Unfortunately, despite the growing interest in big data careers, many people don’t know how to pursue them properly. Data Mining is an important research process.

Data mining 143

Data mining Data Science Informatics Statistics 143

Jen Stirrup

MAY 5, 2020

It is more than just some giant USB stick in the sky that’s going to store all of the data. It has a lot of services that you can use, such as Big Data analytics. To get the best of technology such as Artificial Intelligence or Data Science, you really, really must have your data in the right format, and a good place.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

IBM Big Data Hub

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

Jet Global

JULY 2, 2019

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Digital Transformation 109

Digital Transformation Manufacturing Data Warehouse Predictive Analytics 109