IBM Big Data Hub

AUGUST 28, 2023

It’s no wonder then that Macmillan needs sophisticated business intelligence (BI) and data analytics. For more than 10 years, the publisher has used IBM Cognos Analytics to wrangle its internal and external operational reporting needs. As business processes grew more complex, the data transparency and visibility suffered.

Publishing 50

Publishing Analytics Business Intelligence Operational Reporting 50

Cloudera

JANUARY 19, 2024

As described in our recent blog post , an SQL AI Assistant has been integrated into Hue with the capability to leverage the power of large language models (LLMs) for a number of SQL tasks. This is a real game-changer for data analysts on all levels and will make SQL development faster, easier, and less error-prone.

Data Warehouse 72

Data Warehouse Data Processing Optimization Modeling 72

datapine

SEPTEMBER 27, 2022

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

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 83

Enterprise Data Warehouse Snapshot Cost-Benefit 83

AWS Big Data

MARCH 10, 2023

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. Data can be organized into three different zones, as shown in the following figure.

Data Lake 102

Data Lake Sales Data Warehouse Snapshot 102

AWS Big Data

MAY 30, 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.

Data Warehouse 85

Data Warehouse Data Lake Cost-Benefit Structured Data 85

Cloudera

JANUARY 26, 2022

Network operating systems let computers communicate with each other; and data storage grew—a 5MB hard drive was considered limitless in 1983 (when compared to a magnetic drum with memory capacity of 10 kB from the 1960s). The amount of data being collected grew, and the first data warehouses were developed.

Data Processing 87

Data Processing IoT Cost-Benefit Digital Transformation 87

CIO Business Intelligence

JANUARY 16, 2023

All the logic is still in Java hosted on Amazon’s infrastructure.” Aside from the core cloud services, Choice also uses Amazon RedShift as a front end to its cloud data warehouse, Amazon SageMaker to build machine leaning models, and Amazon Kinesis to collect, process, and analyze real-time data.

Cost-Benefit 136

Cost-Benefit Digital Transformation Data Warehouse Data-driven 136

AWS Big Data

JANUARY 24, 2023

Serverless applications are a modernized way to perform analytics among business departments and engineering teams. Reporting and analysis – An application where you can trigger large analytical queries with dynamic inputs and then view or download the results. What are WebSockets and why do we need them?

Analytics 58

Analytics Data-driven Management Reporting 58

Octopai

NOVEMBER 24, 2021

As the first of its reasons why to migrate to Redshift , Amazon says, “Amazon Redshift is fully managed and simple to use, enabling you to deploy a new data warehouse in minutes and load virtually any type of data from a range of cloud or on-premises data sources.”. Setting up the data warehouse can take minutes.

Data Warehouse 73

Data Warehouse Metadata Data Processing Reporting 73

Cloudera

NOVEMBER 12, 2020

Moving to a cloud-only based model allows for flexible provisioning, but the costs accrued for that strategy rapidly negate the advantage of flexibility. . Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs. A solution. One cluster contains about 800 nodes.

Data Warehouse 104

Data Warehouse Reporting Risk Cost-Benefit 104

Smart Data Collective

NOVEMBER 18, 2020

Data Science is used in different areas of our life and can help companies to deal with the following situations: Using predictive analytics to prevent fraud Using machine learning to streamline marketing practices Using data analytics to create more effective actuarial processes. Where to Use Data Mining?

Data mining 143

Data mining Data Science Informatics Statistics 143

datapine

MARCH 31, 2022

Moreover, companies that use BI analytics are five times more likely to make swifter, more informed decisions. Despite these findings, the undeniable value of intelligence for business, and the incredible demand for BI skills, there is a severe shortage of BI-based data professionals – with a shortfall of 1.5

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Cloudera

MAY 12, 2021

2020 saw us hosting our first ever fully digital Data Impact Awards ceremony, and it certainly was one of the highlights of our year. We saw a record number of entries and incredible examples of how customers were using Cloudera’s platform and services to unlock the power of data. DATA FOR GOOD. PEOPLE FIRST.

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

Alation

JULY 13, 2021

Yet increasing complexity of data makes the old “lift-and-shift” model not just unrealistic, but risky. Businesses with complex data environments need a migration method that takes that complexity into account. The Data Race to the Cloud. This recent cloud migration applies to all who use data. Fern Halper, Ph.D.

Strategy 52

Strategy Data mining Structured Data Data Governance 52

Occam's Razor

SEPTEMBER 9, 2013

This post covers an important evolution in Google Analytics' segmentation power. You are going to love analytics and analysis so much more by the time you are done with this post! One of the key things we are going to learn today is to align our metrics and dimensions optimally to ensure we report good, clean, sensible data.

Analytics 145

Analytics Metrics Marketing Reporting 145

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Sisense

OCTOBER 10, 2019

New tools were slowly introduced to deal with data as it grew in volume and complexity, but the overall landscape was largely unchanged until the rise of “The Cloud.” Once Amazon Web Services and its cloud-storage-as-a-service model hit the tech world, what it meant to be a data engineer changed forever. And that’s a good thing!

Data Processing 60

Data Processing Data Warehouse Digital Transformation Software 60

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. Modern analytics is much wider than SQL-based data warehousing. Amazon Redshift provides comprehensive data security at no extra cost.

Analytics 96

Analytics Data Warehouse Testing Dashboards 96

AWS Big Data

JULY 20, 2023

Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table.

Data Lake 80

Data Lake Metadata Data Processing Big Data 80

datapine

SEPTEMBER 29, 2022

6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes. Industry-wide, the positive ROI on quality data is well understood. 2 – Data profiling. 4 – Data Reporting.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Jet Global

JULY 23, 2019

With multiple sessions on VBA, macros, Jet products, data visualization, Power BI, PivotTables, dashboards, and the latest technology in Microsoft Excel – Excelapalooza is your reporting and analytics dreamland. Power BI Desktop opens a new era in data analysis and reporting. When: September 16th – 18th, 2019.

Dashboards 71

Dashboards Reporting Data Warehouse Visualization 71

Sisense

OCTOBER 6, 2020

The challenge comes when the data becomes huge and fast-changing. Why is quantitative data important? Quantitative data is often viewed as the bedrock of your business intelligence and analytics program because it can reveal valuable insights for your organization. What are the problems with quantitative data?

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Cloudera

SEPTEMBER 12, 2018

It’s a big week for us, as many Clouderans descend on New York for the Strata Data Conference. The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Technical Impact.

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

AWS Big Data

JULY 25, 2023

Today, in order to accelerate and scale data analytics, companies are looking for an approach to minimize infrastructure management and predict computing needs for different types of workloads, including spikes and ad hoc analytics. Prerequisites To complete the integration, you need a Redshift Serverless data warehouse.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

Occam's Razor

DECEMBER 3, 2008

Michael, politely, says in an email: "I have done web analytics for five years, I have mastered Omniture, WebTrends and Google Analytics, I provide analysis and not just reporting. I feel like am an Analytics God. Here's the Avinash Kaushik Web Analytics Career Introspection Guide ! Do some introspection.

Analytics 115

Analytics Consulting Metrics Reporting 115

AWS Big Data

JUNE 6, 2023

It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines. Data quality at rest focuses on validating the data stored in data lakes, databases, or data warehouses. It ensures that the data meets specific quality standards before it is consumed.

Data Quality 68

Data Quality Data Lake Visualization Data-driven 68

DataKitchen

APRIL 13, 2021

Testing and Data Observability. Process Analytics. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Reflow — A system for incremental data processing in the cloud.

Testing 300

Testing Machine Learning Consulting Data Quality 300

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. How did the challenges and opportunities related to security, data management, and system architecture get braided together throughout the past ~6 decades of IT?

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

MAY 7, 2024

Data governance is a key enabler for teams adopting a data-driven culture and operational model to drive innovation with data. Amazon DataZone allows you to simply and securely govern end-to-end data assets stored in your Amazon Redshift data warehouses or data lakes cataloged with the AWS Glue data catalog.

Metadata 74

Metadata Data Lake Data Processing Data-driven 74

Cloudera

JANUARY 21, 2021

While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. Of course you don’t want to re-create the risks and costs of data silos your organization has spent the last decade trying to eliminate.

Data Lake 94

Data Lake Data Warehouse IT Analytics 94

Sisense

FEBRUARY 16, 2021

Every aspect of analytics is powered by a data model. A data model presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. Data modeling organizes and transforms data.

Modeling 52

Modeling OLAP Data Warehouse Cost-Benefit 52

Sisense

FEBRUARY 17, 2021

With more people becoming digital citizens, the ability for an application to explode in popularity has all but rendered obsolete the traditional IT hosting mindset of discrete servers performing discrete tasks. Let’s dig into three hosting models that help organizations achieve cloud flexibility.

Data Processing 52

Data Processing Modeling Technology IT 52

Occam's Razor

APRIL 25, 2011

The issues of course include people and jaded mental models and bureaucracy and a lack of time and the missing desire to be great and org structures, and bosses. Now switching to something a bit more near and dear to my heart, analytics "crimes against humanity" 8. Your analytics / marketing team uses the word Engagement.

Marketing 126

Marketing Measurement Metrics Testing 126

Cloudera

AUGUST 31, 2020

Given the prohibitive cost of scaling it, in addition to the new business focus on data science and the need to leverage public cloud services to support future growth and capability roadmap, SMG decided to migrate from the legacy data warehouse to Cloudera’s solution using Hive LLAP. The case for a new Data Warehouse?

Slice and Dice 66

Slice and Dice Management Data Warehouse Analytics 66

CIO Business Intelligence

FEBRUARY 20, 2024

Recent research by McGuide Research Services for Avanade found 91% of organisations in the sector believe they need to shift to an AI-first operating model within the next 12 months, while 87% of employees feel generative AI tools will make them more efficient, and more innovative.

Cost-Benefit 95

Cost-Benefit Data Lake Data Warehouse Data Processing 95

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52