Double Your Hadoop Performance with Hortonworks SmartSense

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SmartSense uses advanced analytics to make suggestions and recommendations based on the deep knowledge of our Hortonworks engineers and committers to prevent issues and improve performance of your HDP cluster. Based on the diagnostic data collected from HDP clusters around the world, Hortonworks creates personalized recommendations for your specific Hadoop cluster and workloads.

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BellaDati

BellaDati is complete agile data analytics tool in the hands of business user. Pure web-technology based. Empower business users to turn virtually any-size and type of data into profits. Our products are BellaDati Agile BI, Analytics Apps, Mobile BI & Data Platform. BellaDati advantage is. Highly business user focused, Agile BI. Pure web-tech. Complete, Cloud or On-premise version, Social network for business data discovery, Reports are created real-time not developed, Unstructured and structured data analysis, Industry Analytic Apps & +100 data connectors, Native Mobile BI app for iOS & Android, Data analytics platform SDK & APIs, Standalone or complement of traditional SAP, Cognos, SAS BIs. Our famous customers are RedBull, Korean Telecom, New World Resources and other companies from retail, market research, insurance, manufacturing or hospitality services…

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BIG DATA MANAGEMENT

Enhance Your Customer Experience with Data-Centric AI

Article | July 15, 2022

Data-centric AI is a unique approach to machine learning that depends on the data scientist to design the complete pipeline from data purification and intake through model training. There is no need for a detailed understanding of AI algorithms in this method; instead, it is all about the data. The principle behind data-centric AI is simple: rather than training an algorithm first and then cleaning up the dirty dataset, begin with clean data and train an algorithm on that dataset. Why Is It Necessary to Centralize Datasets? A consolidated data platform can be utilized to produce a single source of truth, therefore simplifying and assuring accuracy. When a team concentrates on continual improvement, wasted time and resources are reduced. You can improve optimization by centralizing data. This is due to the increased opportunity for your team to enhance procedures and make better judgments. The capacity to exploit a single platform that promotes constant improvement in processes, products and operationalization models is provided by centralizing data. Data-Centric AI for Personalized Customer Experience Data-centric AI connects your data and analytics. It's used to detect common habits and preferences, tailor marketing campaigns, provide better suggestions, and much more. Data-Centric AI is being used to evaluate various types of data in order to assist organizations in making quicker, more efficient choices. It can be used to analyze client behavior and trends across several channels in order to provide personalized experiences. It enables applications and websites to adjust the information that individuals view according to their preferences, as well as advertisers to target specific consumers with tailored offers. What Will the Future of Data-Centric AI Look Like? Data-centric AI strives to provide a systematic approach to a wide range of domains, including product design and user experience. Data-centric AI is a systematic technique and technology that enables engineers and other data scientists to employ machine learning models in their own data studies. Moreover, the goal of data-centric AI is to build best practices that make data analysis approaches less expensive and easier for businesses to implement effortlessly.

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BIG DATA MANAGEMENT

Why Adaptive AI Can Overtake Traditional AI

Article | July 5, 2022

With the ever-changing technology world, company demands and results are no longer the norm. Businesses in a variety of sectors are using artificial intelligence (AI) technologies to solve complicated business challenges, build intelligent and self-sustaining solutions, and, ultimately, remain competitive at all times. To that aim, ongoing attempts are being made to reinvent AI systems in order to do more with less. Adaptive AI is a significant step in that direction. It has the potential to outperform standard machine learning (ML) models in the near future because of its ability to enable organizations to get greater results while spending less time, effort, and resources. The capacity of adaptive AI to enable enterprises to achieve greater outcomes while investing less time, effort, and assets is why it can overtake traditional AI models. Why Adaptive AI Overtakes Traditional AI Robust, Efficient and Agile Robustness, efficiency, and agility are the three basic pillars of Adaptive AI. The ability to achieve great algorithmic accuracy is referred to as robustness. The capacity to achieve reduced resource utilization is referred to as efficiency (for example, computer, memory, and power). Agility manages the ability to change operational circumstances in response to changing demands. Together, these three Adaptive AI principles provide the groundwork for super-capable AI inference for edge devices. Data-Informed Predictions A single pipeline is used by the adaptive learning approach. With this method, you can use a continually advanced learning approach that maintains the framework up-to-date and encourages it to achieve high levels of performance. The Adaptive Learning method examines and learns new changes made to the information and produces values, as well as their associated attributes. Moreover, it benefits from events that can modify market behavior in real-time and, as a result, maintains its accuracy consistently. Adaptive AI recognizes information from the operational environment and uses it to produce data-informed predictions. Closing Lines Adaptive AI will be utilized to meet changing AI computing requirements. Operational effectiveness depends on algorithmic performance and available computer resources. Edge AI frameworks that can change their computing demands effectively reduce compute and memory requirements. Adaptive AI is robust in CSPs' dynamic software environments, where inputs and outputs alter with each framework revamp. It can assist with network operations, marketing, customer service, IoT, security, and customer experience.

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BUSINESS INTELLIGENCE

Data-Centric Approach for AI Development

Article | August 4, 2022

As AI has grown in popularity over the past decade, practitioners have concentrated on gathering as much data as possible, classifying it, preparing it for usage, and then iterating on model architectures and hyper-parameters to attain our desired objectives. While dealing with all of this data has long been known as laborious and time-consuming, it has typically been seen as an upfront, one-time step we take before entering into the essential modeling phase of machine learning. Data quality concerns, label noise, model drift, and other biases are all addressed in the same way: by collecting and labeling more data, followed by additional model iterations. The foregoing technique has worked successfully for firms with unlimited resources or strategic challenges. It doesn't work well for machine learning's long-tail issues, particularly those with fewer users and little training data. The discovery that the prevailing method of deep learning doesn't "scale down" to industry challenges has given birth to a new "trend" in the area termed "Data-Centric AI." Implementing a Data-Centric Approach for AI Development Leverage MLOps Practices Data-centric AI prioritizes data over models. Model selection, hyper-parameter tuning, experiment tracking, deployment, and monitoring take time. Data-centric approaches emphasize automating and simplifying ML lifecycle operations. Standardizing and automating model-building requires MLOps. MLOps automates machine learning lifecycle management pipelines. An organizational structure improves communication and cooperation. Involve Domain Expertise Data-centric AI development requires domain-specific datasets. Data scientists can overlook intricacies in various sectors, business processes, or even the same domain. Domain experts can give ground truth for the AI use case and verify whether the dataset truly portrays the situation. Complete and Accurate Data Data gaps cause misleading results. It's crucial to have a training dataset that correctly depicts the underlying real-world phenomenon. Data augmentation or creating synthetic data might be helpful if gathering comprehensive and representative data is costly or challenging for your use case.

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BIG DATA MANAGEMENT

A Modern Application Must Have: Multi-cloud Database

Article | July 6, 2022

To function well, modern apps require enormous amounts of diverse data from sensors, processes, interactions, etc. However, these apps cannot understand the unstructured big data and extract commercial value for effective operations unless this data is maintained properly. In today's age of cloud computing, apps gather and analyze data from various sources, but the data isn't always kept in the same database or format. While increasing overall complexity, several formats make it more difficult for apps to retain and use various data. Multi-model databases, a cutting-edge management system, provide a sophisticated approach to handling varied and unstructured data. A multi-model database allows various data models to natively utilize a single, integrated backend, as opposed to combining different database models. Why Has Multi-Model Database Become a Necessity for Modern Applications? Modern applications can store diverse data in a single repository owing to the flexible approach to database management, which improves agility and reduces data redundancy. Improve Reliability Each database might be a single point of failure for a larger system or application. Multi-model databases reduce failure points, enhancing data dependability and recovery time. Such recovery minimizes expenses and maintains customer engagement and application experience. Simplify Data Management Fragmented database systems benefit contemporary applications but complicate development and operations. Multi-model databases provide a single backend that maintains data integrity and fault tolerance, eliminating the need for different database systems, software licenses, developers, and administrators. Improve Fault Tolerance Modern apps must be fault-tolerant and respond promptly to failures promptly. Multi-model databases enable this by integrating several systems into a single backend. The integration provides system-wide failure tolerance. Closing Lines As applications get more complicated, so do their database requirements. However, connecting many databases and preserving consistency between data gathered from various sources is a time-consuming and expensive undertaking. Fortunately, multi-model databases provide an excellent option for generating the data models you want on a single backend.

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Spotlight

BellaDati

BellaDati is complete agile data analytics tool in the hands of business user. Pure web-technology based. Empower business users to turn virtually any-size and type of data into profits. Our products are BellaDati Agile BI, Analytics Apps, Mobile BI & Data Platform. BellaDati advantage is. Highly business user focused, Agile BI. Pure web-tech. Complete, Cloud or On-premise version, Social network for business data discovery, Reports are created real-time not developed, Unstructured and structured data analysis, Industry Analytic Apps & +100 data connectors, Native Mobile BI app for iOS & Android, Data analytics platform SDK & APIs, Standalone or complement of traditional SAP, Cognos, SAS BIs. Our famous customers are RedBull, Korean Telecom, New World Resources and other companies from retail, market research, insurance, manufacturing or hospitality services…

Related News

Hadoop Data and Analytics Head to the Cloud

informationweek | June 20, 2019

Data and analytics in the cloud era may favor platforms based on platforms that are more flexible than Hadoop. But that doesn't mean there's no future for the early big data technology. Has Hadoop gone the way of 8-track tapes and Betamax? The technology that engendered so much excitement and optimism about the potential of big data has, at the very least, hit a speed bump as the two remaining independent providers Cloudera and MapR are each facing their own crisis.Cloudera suffered a couple of disappointing quarters and announced its CEO is stepping down news that was not well received by investors. The company blamed its slow quarters on big deals that had been delayed as it prepared to roll out its post-merger next generation data platform that incorporates multiple technologies beyond Hadoop.Hadoop's biggest problem is that it was built to be a giant single source of data," Hyoun Park, founder and CEO of research firm Amalgam Insights told InformationWeek in an interview. But it's challenging to use Hadoop across multiple data centers or multiple clouds. "The assumption with Hadoop is that you have it, and it holds everything you own. That's a problem in today's world where you have hundreds of apps.

Read More

It’s time to brush off your Hadoop skills and revisit your science textbooks

JAXenter | February 15, 2019

Upwork’s latest skills index has a bunch of surprises for the hungry freelance market. It’s time to brush off your Hadoop skills and revisit your science textbooks. We take a look at what skills you should highlight on your resume, from machine learning skills to data security certifications. Freelancers need to keep their skills sharp and their resumes current. Upwork keeps track of the latest trends in freelance hiring, making it easier for us to see what companies are looking for in new employees. Big changes were happening on the Q4 report. Hadoop swooped out from nowhere, proving that fastest-growing really is a dynamic metric to measure. Let’s take a look at the highlights! Hadoop in the lead, microbiology growing strong. Somewhat surprisingly, Hadoop has joined the list at #1. Hadoop is the open source foundation for many modern big data applications. While it has declined in use as other technologies have come into play, Hadoop still packs a punch. In the last quarter, a number of enterprises have publicly invested in Hadoop, including Databrick and Confluent.

Read More

Travel Trends for 2019: How Big Data, AI Technology, and Personalization Will Impact the Future of Travel Forever

Benzinga | January 02, 2019

They see you when you're sleeping. They know when you're awake. And, more than likely, they know where you live. Big Data, that is. So, it's no wonder that the growing expectation among travelers is for a seamless and personalized travel experience in 2019. And thanks to Big Data, Artificial Intelligence (AI) technology, and savvy online marketing campaigns, consumers may get their wish, according to industry experts who went One-on-One with ExpertFlyer just before the holidays. "Just selling a seat on a plane or putting a head in a bed is not enough today," explains Gilad Berenstein, Founder and CEO of Utrip, which uses AI technology and personal data to create a custom travel experience. "People plan all the time, across all devices. Thus, trip planning needs to be more omnipresent and tools such as AI and personalization help keep travelers engaged throughout their entire planning process."

Read More

Hadoop Data and Analytics Head to the Cloud

informationweek | June 20, 2019

Data and analytics in the cloud era may favor platforms based on platforms that are more flexible than Hadoop. But that doesn't mean there's no future for the early big data technology. Has Hadoop gone the way of 8-track tapes and Betamax? The technology that engendered so much excitement and optimism about the potential of big data has, at the very least, hit a speed bump as the two remaining independent providers Cloudera and MapR are each facing their own crisis.Cloudera suffered a couple of disappointing quarters and announced its CEO is stepping down news that was not well received by investors. The company blamed its slow quarters on big deals that had been delayed as it prepared to roll out its post-merger next generation data platform that incorporates multiple technologies beyond Hadoop.Hadoop's biggest problem is that it was built to be a giant single source of data," Hyoun Park, founder and CEO of research firm Amalgam Insights told InformationWeek in an interview. But it's challenging to use Hadoop across multiple data centers or multiple clouds. "The assumption with Hadoop is that you have it, and it holds everything you own. That's a problem in today's world where you have hundreds of apps.

Read More

It’s time to brush off your Hadoop skills and revisit your science textbooks

JAXenter | February 15, 2019

Upwork’s latest skills index has a bunch of surprises for the hungry freelance market. It’s time to brush off your Hadoop skills and revisit your science textbooks. We take a look at what skills you should highlight on your resume, from machine learning skills to data security certifications. Freelancers need to keep their skills sharp and their resumes current. Upwork keeps track of the latest trends in freelance hiring, making it easier for us to see what companies are looking for in new employees. Big changes were happening on the Q4 report. Hadoop swooped out from nowhere, proving that fastest-growing really is a dynamic metric to measure. Let’s take a look at the highlights! Hadoop in the lead, microbiology growing strong. Somewhat surprisingly, Hadoop has joined the list at #1. Hadoop is the open source foundation for many modern big data applications. While it has declined in use as other technologies have come into play, Hadoop still packs a punch. In the last quarter, a number of enterprises have publicly invested in Hadoop, including Databrick and Confluent.

Read More

Travel Trends for 2019: How Big Data, AI Technology, and Personalization Will Impact the Future of Travel Forever

Benzinga | January 02, 2019

They see you when you're sleeping. They know when you're awake. And, more than likely, they know where you live. Big Data, that is. So, it's no wonder that the growing expectation among travelers is for a seamless and personalized travel experience in 2019. And thanks to Big Data, Artificial Intelligence (AI) technology, and savvy online marketing campaigns, consumers may get their wish, according to industry experts who went One-on-One with ExpertFlyer just before the holidays. "Just selling a seat on a plane or putting a head in a bed is not enough today," explains Gilad Berenstein, Founder and CEO of Utrip, which uses AI technology and personal data to create a custom travel experience. "People plan all the time, across all devices. Thus, trip planning needs to be more omnipresent and tools such as AI and personalization help keep travelers engaged throughout their entire planning process."

Read More

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