Altexsoft

FEBRUARY 11, 2023

Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. Feel free to enjoy it. Feel free to enjoy it.

Data 87

Data Data Engineering Big Data Architecture 87

TIBCO - Connected Intelligence

APRIL 15, 2021

Data and analytics investments pay off. According to McKinsey, “High-performing organizations are three times more likely than others to say their data and analytics initiatives have contributed at least 20 percent to EBIT.” Twenty percent of earnings before interest and taxes from data and analytics alone!

Analytics 41

Analytics Development Team Review Technical Review Data 41

Cloudera

JANUARY 27, 2021

Achieving SOC 2 is one of the first milestones on our aggressive security and compliance roadmap. You can expect to see further compliance achievements, including expanding Cloudera’s ISO27001 certification to include CDP Public Cloud, FedRAMP, and more, over the coming quarters. Why is SOC 2 Important?

Cloud 102

Cloud Disaster Recovery Software Review Technical Review 102

Cloudera

NOVEMBER 16, 2022

A common theme that was highlighted continuously was using “storytelling” to relay the value that data and analytics teams bring to the business. This leads to a further challenge of the chief data and analytics officer’s role: Is the mission to play offense — drive revenue?

Data 79

Data Insurance Metrics eBook 79

Cloudera

NOVEMBER 1, 2022

In the private sector, excluding highly regulated industries like financial services, the migration to the public cloud was the answer to most IT modernization woes, especially those around data, analytics, and storage. The source and availability of every material and part across each branch is an opportunity for risk.

Cloud 86

Cloud Government Storage Analytics 86

Cloudera

APRIL 9, 2021

The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. And this blog will focus on Predictive Analytics. Data Collection – streaming data. Data Enrichment – data engineering.

Machine Learning 98

Machine Learning Artificial Inteligence Data Data Engineering 98

AWS Machine Learning - AI

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. In addition to awareness, your teams should take action to account for generative AI in governance, assurance, and compliance validation practices.

Artificial Inteligence 117

Artificial Inteligence Generative AI Security Applications 117

InnovationM

MAY 14, 2023

They help establish data lineage, enable data discovery, and enforce compliance with data governance policies. Metadata management tools ensure a clear understanding of the data landscape and enhance data stewardship practices.

Data 52

Data Storage Construction Business Intelligence 52

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Architecture 52

Architecture Data Storage Artificial Inteligence 52

Capgemini

MAY 28, 2020

From stringent regulatory requirements to product specifications, non-compliance can lead to significant issues, from dissatisfied customers to fines and class-action lawsuits. Manufacturers typically work with components from multiple sources, so assembling all the data from suppliers and the shop floor is critical for tracing back defects.

Artificial Intelligence 52

Artificial Intelligence Artificial Inteligence Automotive Data Engineering 52

Cloudera

AUGUST 31, 2021

Data warehousing is the backbone of every data driven organization , providing mission critical analytics. Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Copyright © 2021 Accenture. All rights reserved.

Data 82

Data Analytics Cloud Technical Review 82

Cloudera

DECEMBER 1, 2017

Vinithra Varadharajan , Philip Langdale , Jason Wang , and Fahd Siddiqui lead a deep dive into running data engineering workloads in a managed service capacity in the public cloud, highlighting cloud infrastructure best practices and illustrating how data engineering workloads interoperate with data analytic engines.

Big Data 40

Big Data Data Cloud Government 40

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Alation: support for self-service analytics and BI.

Tools 59

Tools Technical Review Software Review Systems Review 59

O'Reilly Media - Ideas

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Temporal data and time-series analytics.

Big Data 84

Big Data Technology Tools Cloud 84

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Happy New Year and welcome to 2019, a year full of possibilities.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

TIBCO - Connected Intelligence

JANUARY 11, 2021

Underestimating data engineering workloads . Every MDM project requires that you understand, access, and integrate your source data in some way. This can be especially challenging in today’s distributed data landscapes where your data spans many repositories, both on-premises and in the cloud.

Technical Advisors 54

Technical Advisors Data Data Engineering Compliance 54

Altexsoft

NOVEMBER 9, 2021

If you want to streamline your procurement and gain more visibility into this process, you have to get hold of available data, analyze it, and extract value to make informed decisions. What is procurement analytics and the opportunities it offers? Main components of procurement analytics. Procurement and its challenges.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

Last year REAN Cloud acquired 47Lining to provide deep capabilities in cloud-based analytics and machine learning that expands Hitachi Vantara’s ability to maximize data-driven value for vertical IoT solutions. This April, 47Lining, announced its Amazon Web Services (AWS) Industrial Time Series Data Connector Quick Start.

Cloud 78

Cloud Google Cloud Azure AWS 78

Cloudera

JANUARY 19, 2018

Apache Kudu: Top Use Cases for Real-Time Analytics. Kudu makes running real-time analytics easier than ever before and is foundational to a number of successful Cloudera IoT customers. This blog highlights three use cases where real-time makes a real difference: time series data, machine data analytics, and online reporting.

Artificial Inteligence 45

Artificial Inteligence Machine Learning Big Data IoT 45

Capgemini

NOVEMBER 14, 2022

And they also need to trust that the data is current, relevant, and available. Moving a business forward requires fast access to good data for more applications. But that turns IT and analytics teams into the bottleneck. For self-service BI to succeed, however, the entire data value chain may need to be fixed.

Agile 52

Agile Technical Review Systems Review Artificial Intelligence 52

Existek

JANUARY 11, 2022

The cloud computing market covers many areas like business processes, infrastructure, platform, security, management, analytics supported by cloud providers. Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT. Game tech

Azure 52

Azure AWS Cloud How To 52

Cloudera

MAY 10, 2018

Click to tweet : Nominations are now open for the sixth annual Cloudera Data Impact Awards! With advancements in exploratory data science, machine learning, predictive analytics, AI, and data engineering, the world is increasingly driven by data. Read how to get nominated. link] #DataImpactAwards.

Data 41

Data Artificial Inteligence Machine Learning Data Engineering 41

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

TIBCO - Connected Intelligence

MARCH 5, 2020

The business value of data continues to rise – Every business transformation today—be it greater customer intimacy, more optimized operations, faster innovation, and more—is fueled by data. Data quality remains elusive – Diverse and evolving data definitions, syntax, structures, sources, and uses conspire to limit data efficacy.

Technology 42

Technology Games Trends IoT 42

Altexsoft

FEBRUARY 8, 2021

These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development. Optimization opportunities offered by analytics.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Cloudera

DECEMBER 10, 2020

CDW’s engines such as Impala and Hive low-latency analytical processing (LLAP) directly operate on extremely compact open file formats, such as Parquet and ORC. These file formats not only help avoid data duplication into proprietary storage formats but also provide highly efficient storage formats.

Data 98

Data Technical Review Storage Systems Review 98

TIBCO - Connected Intelligence

OCTOBER 15, 2020

Towards these trends, our clients have significantly invested in data-related technologies and talent over the past several years, including: Data governance to cater to enterprise-level financial reporting and regulatory compliance as an example. Self-service data discovery (e.g.,

Virtualization 50

Virtualization Data Energy Innovation 50

Altexsoft

JUNE 11, 2021

Process analytics takes place. Here, KPIs can be created and monitored to uncover potential improvement areas, data mining and/or ML algorithms can be used to detect hidden patterns and dependencies, or conformance checking techniques can be applied to compare the process to a certain ideal model. Consider predictive analytics.

Applications 69

Applications Weak Development Team Systems Review Software Review 69

TIBCO - Connected Intelligence

JUNE 16, 2021

Data security: Ensure that customer data is stored and maintained securely, protected properly, and only accessible to appropriate parties. Compliance: Use data and analytics to demonstrate contractual, regulatory, and internal audit compliance.” ” That is a big list.

Data 29

Data Weak Development Team Virtualization Compliance 29

O'Reilly Media - Ideas

NOVEMBER 13, 2018

The Future of Privacy Forum and Immuta recently released a report with some great suggestions on how one might approach machine learning projects with risk management in mind: When you’re working on a machine learning project, you need to employ a mix of data engineers, data scientists, and domain experts.

Machine Learning 89

Machine Learning Artificial Inteligence Software Review Conference 89

Cloudera

FEBRUARY 15, 2024

While there are clear reasons SVB collapsed, which can be reviewed here , my purpose in this post isn’t to rehash the past but to present some of the regulatory and compliance challenges financial (and to some degree insurance) institutions face and how data plays a role in mitigating and managing risk.

Insurance 85

Insurance Compliance Technical Review Banking 85

Cloudera

OCTOBER 19, 2023

Add to this, too, the difficulty of integrating potentially dissimilar compliance frameworks: for example, separate telcos might be operating under different regulatory guidelines, appropriate to specific jurisdictions or business practices, requiring the merged entity to formalize a single, unified framework for compliance.

Data 74

Data Compliance Architecture Data Engineering 74

Cloudera

FEBRUARY 19, 2018

But when companies are looking towards new technologies such as data lakes, machine learning or predictive analytics, SAP alone is just not enough. To keep up with tech trends, businesses have to face the challenges of integrating SAP with non-SAP technologies and embark on a crusade against data silos. Governance. Cataloging.

Data 46

Data Government Big Data Compliance 46

InfoWorld

FEBRUARY 9, 2024

Microsoft Fabric is an end-to-end, software-as-a-service (SaaS) platform for data analytics. It is built around a data lake called OneLake, and brings together new and existing components from Microsoft Power BI, Azure Synapse, and Azure Data Factory into a single integrated environment.

Big Data 86

Big Data Azure Google Cloud Data 86

Erik Bernhardsson

OCTOBER 27, 2015

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences? So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. How do you respond when you hear the phrase ‘big data’? Write a ton of code. Don’t watch TV :).

Data 100

Data Big Data Artificial Inteligence Machine Learning 100

Erik Bernhardsson

OCTOBER 27, 2015

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences? So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. How do you respond when you hear the phrase ‘big data’? Write a ton of code. Don’t watch TV :).

Data 100

Data Big Data Artificial Inteligence Machine Learning 100

DataRobot

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52