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 81

Data Science Data Analytics Prescriptive Analytics Analytics 81

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 134

Analytics Data Science Unstructured Data Data mining 134

Analytics Vidhya

FEBRUARY 25, 2023

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

Data Lake 172

Data Lake Unstructured Data Management Analytics 172

CIO Business Intelligence

OCTOBER 19, 2023

The growing volume of data is a concern, as 20% of enterprises surveyed by IDG are drawing from 1000 or more sources to feed their analytics systems. Data integration needs an overhaul, which can only be achieved by considering the following gaps. This enables the system to examine the data before transforming and altering it.

Data Integration 126

Data Integration Unstructured Data Data-driven Data Warehouse 126

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 128

Analytics Data Science Statistics Unstructured Data 128

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 111

Data Lake Analytics Dashboards Metrics 111

CIO Business Intelligence

FEBRUARY 16, 2023

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 105

AWS Big Data

JANUARY 8, 2024

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 111

Analytics IoT Data-driven Snapshot 111

CIO Business Intelligence

OCTOBER 13, 2023

Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes. Application data architect: The application data architect designs and implements data models for specific software applications.

Data Architecture 131

Data Architecture Data Warehouse Statistics Visualization 131

Cloudera

MARCH 17, 2023

The one key component that is missing is a common, shared table format, that can be used by all analytic services accessing the lakehouse data. The table format provides the necessary structure for the unstructured data that is missing in a data lake, using a schema or metadata definition, to bring it closer to a data warehouse.

Unstructured Data 63

Unstructured Data Data Lake Data Warehouse Machine Learning 63

FineReport

JULY 28, 2021

If you are curious about the difference and similarities between them, this article will unveil the mystery of business intelligence vs. data science vs. data analytics. Definition: BI vs Data Science vs Data Analytics. What is Data Science? Typical tools for data science: SAS, Python, R.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

Andrew White

DECEMBER 9, 2019

This was a great inquiry since it called into question the perceived wisdom peddled by some that cataloging everything was a prerequisite for data (and analytics) governance. Modern data (and analytics) governance does not necessarily need: Wall-to-wall discovery of your data and metadata. The use case, and.

Analytics 66

Analytics Data Lake Data Governance Metadata 66

FineReport

JULY 1, 2021

If you have never heard of BI, I suggest you read these two articles, one on BI and analytics and one on BI reporting , to build your background knowledge. BI technology is a series of technologies that can handle a large amount of structured and sometimes unstructured data. Data warehouse. Ad hoc analytics.

Business Intelligence 98

Business Intelligence Technology Data Warehouse Dashboards 98

IBM Big Data Hub

OCTOBER 30, 2023

IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. AWS, on the other hand, provides robust, scalable cloud infrastructure.

Insurance 82

Insurance Data Warehouse Data-driven Unstructured Data 82

Jet Global

SEPTEMBER 4, 2020

For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

Jet Global

NOVEMBER 5, 2020

OLAP reporting has traditionally relied on a data warehouse. Again, this entails creating a copy of the transactional data in the ERP system, but it also involves some preprocessing of data into so-called “cubes” so that you can retrieve aggregate totals and present them much faster. Azure Data Lakes are complicated.

Data Lake 98

Data Lake OLAP Data Warehouse Unstructured Data 98

Jedox

OCTOBER 22, 2020

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it. Data analytics is not new. Inability to get data quickly.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

CIO Business Intelligence

MAY 24, 2022

Some of the accelerators included as part of the new platform are integrations with Salesforce, NPI data, National Patient Account Services, Workday, Oracle Fusion HCM Cloud, Orange HRM, Salesforce Health Cloud, MedPro, healthcare-focused cloud company Veeva, and HR vendor UltiPro. Analytics for faster decision making.

Finance 137

Finance Management Metadata Data Quality 137

CIO Business Intelligence

APRIL 25, 2022

The data lakehouse is a relatively new data architecture concept, first championed by Cloudera, which offers both storage and analytics capabilities as part of the same solution, in contrast to the concepts for data lake and data warehouse which, respectively, store data in native format, and structured data, often in SQL format.

Recreation/Entertainment 116

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 116

CIO Business Intelligence

JULY 10, 2023

His role now encompasses responsibility for data engineering, analytics development, and the vehicle inventory and statistics & pricing teams. The company was born as a series of print buying guides in 1966 and began making its data available via CD-ROM in the 1990s.

Data Warehouse 94

Data Warehouse Cost-Benefit Unstructured Data Machine Learning 94

AWS Big Data

MARCH 25, 2024

This data store provides your organization with the holistic customer records view that is needed for operational efficiency of RAG-based generative AI applications. For building such a data store, an unstructured data store would be best. This is typically unstructured data and is updated in a non-incremental fashion.

Data Lake 88

Data Lake Unstructured Data Management Modeling 88

Timo Elliott

JANUARY 3, 2022

It’s stored in corporate data warehouses, data lakes, and a myriad of other locations – and while some of it is put to good use, it’s estimated that around 73% of this data remains unexplored. In this way, you can turn dark data into insights and help drive business improvements. Learn More.

IT 137

IT Unstructured Data Data Quality Machine Learning 137

erwin

AUGUST 15, 2022

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. This blog is an introduction to some advanced NoSQL and data lake database design techniques (while avoiding common pitfalls) is noteworthy. Data modeling basics.

Data Lake 52

Data Lake Modeling Unstructured Data Data Warehouse 52

CIO Business Intelligence

DECEMBER 13, 2023

An example of that is a data warehouse in Azure we brought in and offer as a service. All they have to do is map their data and upload it, and then new data is refueled overnight so they can get new analytics out.” “We’ve inherited some of it after reorganizations that we develop and manage.

IT 89

IT Unstructured Data Cost-Benefit Data Warehouse 89

Cloudera

SEPTEMBER 17, 2020

To address this, they focused on creating an experimentation-oriented culture, enabled thanks to a cloud-native platform supporting the full data lifecycle. This platform, including an ad-hoc capable data warehouse service with built-in, easy-to-use visualization, made it easy for anyone to jump in and start experimenting.

Experimentation 81

Experimentation Data Warehouse Dashboards Visualization 81

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 101

Data Lake Sales Data Warehouse Snapshot 101

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Deciding on KPIs to gauge a data architecture’s effectiveness.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

Analytics Vidhya

MAY 29, 2023

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Both are associated with data usage but differ from each other.

Data mining 267

Data mining Data Collection Strategy Data Analytics 267

datapine

NOVEMBER 19, 2019

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward.

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Software 146

AWS Big Data

APRIL 19, 2023

With the right analytics approach, this is possible. In this post, we look at three key challenges that customers face with growing data and how a modern data warehouse and analytics system like Amazon Redshift can meet these challenges across industries and segments.

Data Warehouse 55

Data Warehouse Data Lake Unstructured Data Optimization 55

Cloudera

SEPTEMBER 15, 2022

The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. A key area of focus for the symposium this year was the design and deployment of modern data platforms. Why should this be on their technology roadmap?

Data Lake 82

Data Lake Data Architecture Data-driven Data Warehouse 82

AWS Big Data

OCTOBER 20, 2023

We’ve seen a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With these connectors, you can bring the data from Azure Blob Storage and Azure Data Lake Storage separately to Amazon S3. Learn more in README.

Data Lake 83

Data Lake Big Data Consulting Data Warehouse 83

Sisense

MARCH 14, 2021

However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

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. Qualitative data benefits: Unlocking understanding.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Cloudera

JANUARY 28, 2022

As I meet with our customers, there are always a range of discussions regarding the use of the cloud for financial services data and analytics. Customers vary widely on the topic of public cloud – what data sources, what use cases are right for public cloud deployments – beyond sandbox, experimentation efforts.

Slice and Dice 114

Slice and Dice Cost-Benefit Risk Data Governance 114

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. The many data warehouse systems designed in the last 30 years present significant difficulties in that respect.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

Jet Global

OCTOBER 30, 2020

Microsoft’s next option is called Azure Data Lake Services (ADLS), and it seems to be the company’s favored long-term solution to its D365 F&SCM reporting challenge. Data lake” is a generic term that refers to a fairly new development in the world of big data analytics. What are unstructured data?

Data Lake 52

Data Lake Unstructured Data Reporting Finance 52