Table of Contents
Introduction
1. Tableau
2. LookerML
3. Qlik Sense
4. Klipfolio
5. Microsoft Power BI
Conclusion

Introduction

In the data-driven world, data visualization is the ultimate BI tool that takes large datasets from numerous sources, aiding data visualization engineers to analyze data and visualize it into actionable insights. In the data analysis process, data visualization is the final chapter that includes a variety of graphs, charts, and histograms in the form of reports and dashboards to make the data more friendly and understandable. 

Therefore, to create a data analysis report that stands out, AITechPark has accumulated the top five most popular data visualization tools. These data visualization tools will assist data visualization engineers, further help businesses understand their needs, and provide real-time solutions to streamline the business process.

1. Tableau

Tableau is one of the most popular data visualization tools used by data scientists and analysts to create customized charts and complex visualizations. The users can connect the data sources, which include databases, spreadsheets, cloud services, and other big data references, allowing them to import and transform data for their analysis. However, Tableau is not the right tool for data creation and preprocessing, as it does not support spreadsheet tools for multi-layered operations. Tableau is expensive when compared to other data visualization tools on the market. The cost of Tableau subscriptions varies; for instance, Tableau Public and Tableau Reader are free, while Tableau Desktop is available for $70/user/month, Tableau Explorer for $42/user/month, and Tableau Viewer for $15/user/month.

2. LookerML

LookerML is a powerful tool that helps data teams visualize capabilities and data inputs and create a powerful modeling layer that allows them to turn SQL into object-oriented code. To keep the workflow up and running without any challenges, teams can take advantage of Looker Blocks, a robust library of analytics code. However, beginners will still need some apprenticeship to learn the art of data visualization before working with Looker, as it provides complicated tools that might be difficult to understand at first glance. The tool also comes with pre-defined built-in visualizations that have some fixed standards and specifications, giving limited options for customization. The pricing varies from $5,000 per month to $7,000 per month, depending on the size and usage of the tool. 

3. Qlik Sense

Qlik Sense is a one-stop data visualization platform for data teams that provides an associative data analytics engine with a sophisticated AI system and a scalable multi-cloud architecture to deploy a mixture of SaaS, private, and on-premises cloud. The data team can combine, visualize, explore, and load datasets on Qlik Sense to create data charts, tables, and visualizations, further instantly updating itself according to the new data context. However, Qlik Sense has some major drawbacks, such as having fewer collaboration features, which are not sufficient for data visual engineers to perform tasks when compared to other competitors’ tools. On a trial basis, Qlik Sense Business is free for 30 days, and then it moves to paid versions that vary from $20 per month per user to $2700 per month for unlimited basic users. 

4. Klipfolio

Klipfolio is one of the data visualization tools in Canada as it allows data visualization engineers to access their data from multiple sources, such as databases, files, and web service applications, as connectors. The tool allows users to create custom drag-and-drop data visualizations where they can choose from different options like charts, graphs, scatter plots, etc. Klipfolo also creates KPI-based dashboards that enable companies to get a glimpse of their business performance. However, the weakness of the tool is that it only functions online and gets disrupted when the internet connection is unstable. Klipfolio also has a limited variety of data sources when compared to other data visualization tools on our list. In terms of pricing, Klipfolio offers a free trial of 14 days, followed by $49 per month for the basic business plan.

5. Microsoft Power BI

Microsoft’s Power BI is an easy-to-use data visualization tool that is available both for deployment and on-premise installation on the cloud infrastructure. The tool is complete within itself, as it supports a myriad of backend databases, such as Teradata, Salesforce, PostgreSQL, Oracle, Google Analytics, Github, Adobe Analytics, Azure, SQL Server, and Excel. According to users, Power BI tends to be praised for its ability to flow data and its modeling capabilities, making it one of the strong contenders in the data modeling and infrastructure markets. However, Power BI lacks visualization as there are fewer customization alternatives than other data visualization tools on our list. The price of Microsoft Power BI is quite pocket-friendly at $9.99 per user and can extend up to $15.99 per user, depending on the package. 

Conclusion

With the growing reliance on data volume available in the market, organizations have started realizing the power of data analytics, which can source real-time data internally and externally as a predictive and prescriptive source. However, to improve data analysis and visualization, engineers are required to select the right tool that aligns with their business goals and needs. Opting for the right tool will help in curating the vast amount of information without human error, eventually aiding in streamlining businesses.

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Suppose you’ve been working on landing a high-value B2B client for months, writing a proposal that you believe is tailored to their needs. It explains your solution based on the technological features, comes with compelling references, and responds to their challenges. Yet, when the client responds with a simple “thanks, we’ll be in touch,” you’re left wondering: Was I heard? Was the intended message or the value provided by the product clear?

Here the shortcomings of conventional approaches to Natural Language Processing (NLP) in B2B communication manifest themselves…Despite these strengths, NLP tools are not very effective in understanding the nuances of B2B business and language and are rather limited in understanding the essence and intention behind the text. Common technical words in the document, rhetoric differences, and constant dynamics of the field that specialized terms reflect are beyond the capabilities of traditional NLP tools.

This is where Quantum Natural Language Processing (QNLP) takes the spotlight. It combines quantum mechanics with its ability to process language, making it more refined than previous AI systems. 

It’s like having the ability to comprehend not only the direct meaning of the text but also the tone, the humor references, and the business-related slang. 

QNLP is particularly rich for B2B professionals. This simply means that Through QNLP, companies and businesses can gain a deeper understanding of what the customer needs and what competitors are thinking, which in turn can re-invent the analysis of contracts to create specific marketing strategies.

 1. Demystifying QNLP for B2B professionals

B2B communication is all the more complex. Specificities in the contracts’ text, specific terminals, and constant changes in the industry lexicon represent the primary difficulty for traditional NLP. Many of these tools are based on simple keyword matches and statistical comparisons, which are capable of failing to account for the context and intention behind B2B communication.

This is where the progress made in artificial intelligence can be seen as a ray of hope. Emerging techniques like Quantum Natural Language Processing (QNLP) may bring significant shifts in the analysis of B2B communication. Now let’s get deeper into the features of QNLP and see how it can possibly revolutionize the B2B market.

1.1 Unveiling the Quantum Advantage

QNLP uses quantum concepts, which makes it more enhanced than other traditional means of language processing. Here’s a simplified explanation:

• Superposition: Think of a coin that is being rotated in the air with one side facing up; it has heads and tails at the same time until it falls. In the same way, QNLP can represent a word in different states at once, meaning that it is capable of capturing all the possible meanings of a certain word in a certain context.
  
• Entanglement: Imagine two coins linked in such a way that when one flips heads, the other is guaranteed to be tails. By applying entanglement, QNLP can grasp interactions as well as dependencies between words, taking into account not only isolated terms but also their interconnection and impact on the content of B2B communication.

By applying these nine concepts, QNLP is capable of progressing from keyword-based matching to understanding the B2B language landscape.

1.2 DisCoCat: The Framework for QNLP

The DisCoCat model is a mathematical framework for Distributed Correspondence Categorical Quantum Mechanics (DisCoCat) in language. It effectively enables QNLP to overlay the subtleties of B2B communication—be it contractual wording throughout specification documentation—in a format that is comprehensible and processable for quantum computing systems.

This creates opportunities for various innovative concepts in B2B communication. 

Imagine an AI that is not only capable of reading through the legal jargon of a contract but is also able to differentiate the connections between different clauses. It will also know whether there are gray areas in the document, and understand the overarching goal of the contract. Incorporated under DisCoCat, there is an enormous possibility that QNLP will transform how different businesses communicate, leading to a new paradigm shift of efficiency, accuracy, and understanding within the B2B environment. 

2. Potential Applications of QNLP in B2B

Most of the NLP tools lack the ability to unravel the nuanced flow of B2B communication. QNLP stands out as a revolutionizing tool for B2B professionals, transforming the strategies at their disposal. Let’s explore how QNLP unlocks valuable applications:

2.1 Enhanced Customer Insights: 

QNLP not only sees words but also sentiment, intent, and even purchasing behavior. This enables a B2B firm to know their customers inside and out, enabling them to predict the needs of the buyers and design better strategies for effective customer relations. 

2.2 Advanced Document Processing: 

The strength of QNLP lies in the fact that it can perform the extraction of relevant information with a higher degree of sensitivity due to the application of quantum mechanics. This eliminates manual processing bottlenecks, reduces mistakes, and improves important organizational activities. 

2.3 Personalized B2B Marketing: 

Through QNLP, B2B marketers can create content and campaigns that are tailored to niches and clients. By being able to better understand the customers and the market that the business operates in, QNLP allows companies to deliver messages that are not only relevant but can strike a chord with the audience, paving the way for better lead generation. 

2.4 Improved Chatbot Interactions: 

Chatbots are evolving the way B2B customer interactions occur. However, the usefulness of these tools is limited by their capability to deal with intricate questions. QNLP enhances chatbots to deal with customers’ interactions with more context awareness. Essentially, by analyzing these hard-to-detect prompts underlying the customers’ questions, QNLP-based chatbots are capable of delivering more adequate and beneficial answers that can enhance customer service. 

QNLP is a game-changer for the B2B channel of communication. By obtaining deeper insights into customer data, documents, and interactions, QNLP creates added benefits to B2B businesses in their strategic decision-making and organizational improvements with enhanced performance. 

3. The Road Ahead: QNLP and the Future of B2B Communication

It is worth stating that Quantum Natural Language Processing (QNLP) may exert a transformative influence on B2B communication. QNLP is yet in its infancy, and its capacity to understand the subtleties of complicated B2B jargon does not cease to amuse. Think about early warning systems that are able to log and process not only the quantity of information but also the qualitative psycho-emotional impact and purpose of B2B communication. 

Nonetheless, the use of QNLP to its full potential in a B2B environment depends on a collaborative attitude. It will be the work of quantum computing experts, NLP researchers, and business-to-business industry gurus who will do extensive research and development on this revolutionary technology for its continuous evolution.

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Introduction

The business landscape has been transformed over the past few years with the help of numerous technologies, and one such marvel is augmented intelligence, which has emerged as a potent ally for human users to enhance our business capabilities. This technology represents a synergy between human expertise and machine learning (ML), redefining how human intelligence approaches problem-solving, decision-making, and innovation. However, amidst all the insights, it is essential to understand that augmented intelligence is not a solution that can be operated independently. Still, it requires human oversight and intervention to help carefully orchestrate ethical considerations to ensure their alignment with human values and ideals.

In today’s AI Tech Park article, we will explore the boundless potential of augmented intelligence in reshaping the future of business.

A Symbiotic Relationship with Organizations and Augmented Intelligence

Augmented intelligence focuses on enhancing human capabilities by combining creativity and design-making skills with artificial intelligence’s (AI) ability to process large sets of data in a few seconds. For instance, in the healthcare sector, AI filters through millions of medical records to assist doctors in diagnosing and treating patients more effectively, therefore not replacing doctors’ expertise but augmenting it. Further, AI automates repetitive tasks, allowing human users to tackle more complex and creative work, especially with chatbots as they handle routine inquiries in customer service, allowing human agents to resolve more minute issues.

Augment intelligence uses personalized experience at a scale that informs users about current market trends, enhancing customer satisfaction, further helping to stimulate human creativity, and exploring new patterns and ideas. Numerous tools, such as OpenAI’s GPT-4 and Google Gemini, can create high-quality written content, which will assist writers and marketers in inefficiently generating social media posts and creative writing pieces. In terms of designing, genAI tools such as DALL-E and MidJourney work as guides that enable designers to generate unique images and artwork based on a few textual descriptions.

Real-World Business Scope of Augmented Intelligence

Till now, we have understood that augmented intelligence is the next thing that companies should implement in their businesses to kickstart a creative yet partially autonomous journey. Therefore, we bring you some key areas where augmented intelligence can have a significant impact:

1. Retail and Manufacturer Industry

The retail industry has witnessed a change in customer tastes post-COVID-19 pandemic, disrupting their logistical and supply chain structures. Therefore, retailers utilize augmented intelligence to analyze customer preference, purchase history, and browsing human behavior to deliver personalized product recommendations that not only enhance their shopping experience but also drive more sales and foster customer loyalty. On the other hand, the manufacturing industry has faced challenges such as supply chain disruptions, a massive drop in worker supply, and raw material shortages due to the pandemic and recession. Therefore, to curb these issues, B2B manufacturers rely on augmented intelligence that aids in data collection from sensors and IoT devices, which eventually helps them understand the production capacities of the production lines, the shipping times, warehousing space availability, and scheduling time for the workers.

2. Healthcare Industry

With the rise in patient personalization and medical and drug experiments, healthcare providers are leveraging augmented intelligence to analyze the largest amount of medical data and predict and diagnose diseases more accurately and efficiently. With the help of augmented analytics, hospitals and medical institutes can optimize their business operations by researching key metrics such as the duration of stay and the bed occupancy rate.

Bottom Line

The human-AI collaboration offers potential by leveraging the strengths of both human creativity and augmented intelligence to achieve shared objectives of better business operations. However, the implementation of this technology doesn’t imply the replacement of human intelligence, but this collaborative initiative will enhance decision-making, boost efficiency, and transform business interaction to enhance organization scalability and personalization.

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Table of contents:
1. Real-time Analytics and Streaming Data in Depth
1.1 What is Real-time Analytics?
1.2 What is Streaming Data?
2. Key Components and Technologies
3. Powering Business Growth with Streaming Data
3.1 Financial Services
3.2 Healthcare
3.3 Retail
3.4 Manufacturing
4. The Future of Real-time Analytics with Streaming Data

As the business world revolves around globalization and faster results, top executives, data analysts, and even marketing managers look forward to real-time analytics. It enables them to harness the power of streaming data in their business and gain a vast amount of valuable information that can inspire the growth of the business.

A manufacturing giant takes global production to the next level by leveraging real-time analytics to predict equipment breakdowns before they happen, boosting productivity across all departments. This is the power of real-time analytics and this is where the real potential for any business is hidden: the potential to turn into the industry leader.

Real-time analytics enables you to possess the flexibility and vision to trump your rivals while building toward stable revenue decades ahead.

Q. What is Real-time analytics and streaming data?

Real-time analytics could be defined as data analysis that takes place with maximum efficiency, and within a short period, which will allow businesses to constantly adapt to events and make the correct decisions based on that data.

Real-time analytics uses streaming data as its primary source for feeding data into the analysis process. It is a stream of data that emanates from numerous sources, such as sensors, social sites, customers, and monetary transactions, for example. While the traditional batch method has a rigid approach that analyzes data at fixed intervals, streaming data analysis occurs on the spot from time to time.

This blog is your roadmap to making sense of real-time analytics, streaming data, and what’s next. Here, we will discuss and give evidence of the benefits that users will realize from this technology, review the enabling technologies required for real-time analytics, and explain, in detail, the different elements that are required to achieve reliable big data real-time analytics within organizations.

1. Real-time Analytics and Streaming Data in Depth

The ability to digest information as it is received and not wait longer is very useful in today’s information society. This is where real-time analytics comes in.

It elaborates on the results being acquired instantly, which allows for a flexible and immediate response to the needs of the business.

1.1. What is Real-time Analytics?

Real-time analytics is a way of getting insights from data as soon as it arrives. Real-time, in the context of big data, refers to analytics that are provided once the data has been processed, but without the delays of traditional batch processing. 

Real-time data visibility helps businesses respond to events in real-time, make timely decisions, and formulate strategies, especially when they notice deviations from the normal trend.

1. 2. What is Streaming Data?

In real-time analytics, the lifeblood is derived from streaming data, which means data is continually fed from various sources. Think of the feeder being on constantly and pumping data into your analytics centre. Some B2B examples include:

• Social media feeds – analyzing real-time sentiment about your brand and ads,
• IoT sensor data for factory machinery, supply chain, and building energy,
• Financial transactions to prevent and report fraud and embezzlement, more and less profits, 
• Customers’ website activity to monitor the behaviour and marketing strategy, and predict potential paying consumers.

2. Key Components and Technologies

Organizations need to be equipped with an analytics platform that delivers real-time data for efficient strategic decision-making all over the pyramid. By leveraging the use of data ingestion tools such as Kafka and Flume, you would be in a good position to transfer stream data without interfering with your current systems. Apache Spark or Flink and other appropriate iterative stream processing frameworks facilitate real-time analysis, which in turn helps to respond actively to the changes occurring in the market and customers’ behaviour.

For faster access to data, implement in-memory databases like Redis for a fast scan of the data, or the scalability aspects provided by Cassandra or MongoDB. Last of all, BI tools such as Grafana or Tableau facilitate concise and effective communication of insights to the parties concerned, as it helps correlate with the narrative.

In today’s faster and more complex B2B environment, real-time analytical capability is not a frill, but a necessity. If businesses incorporate these constituents and technologies into their solutions. They can fully harness the power of streaming data and make a tangible business impact.

3. Powering Business Growth with Streaming Data

The change to massive quantities of data is ongoing and real-time analytics has become the latest buzzword. By using streaming data, it becomes possible to garner a lot of information and help diverse business organizations make decisions faster and more accurately.

3. 1 Financial Services: 

Chief Risk Officers and Fraud Analysts: 

Real-time solutions allow fraud analysts or risk officers to respond in real-time to fraudulent activities protecting the financial health of an organization.

Investment Professionals and Traders: 

Unlock rapid business results with timely recommendations as the market moves. Breathtaking market insights and instant visualization of investments and trades make this technology uniquely efficient for professional investors and traders

3. 2 Healthcare: 

Physicians and Care Teams: 

Continual patient monitoring also eliminates the need to wait for the results in an emergency, allowing physicians or healthcare teams to adjust the course of treatment in the blink of an eye.

Healthcare Administrators and Public Health Officials: 

Using predictive capabilities, healthcare professionals can identify probable disease epidemics and, as a result, direct resources effectively, enabling preventive healthcare administration.

3.3 Retail: 

Marketing Directors and Customer Relationship Managers: 

CRMs and MDs can create effective and highly targeted customer interactions in real-time. Another aspect of customer-oriented strategies is to utilize available information to better address clients’ wants and needs to increase their interest and commitment.

Supply Chain Managers and Inventory Control Specialists: 

SCMs and Inventory control specialists can work with the suitable inventory with real-time analytics help. Eliminate the occurrence of stockouts, cut down on related expenses, and optimize all aspects of managing your stocks.

3.4 Manufacturing: 

Operations Managers and Maintenance Engineers: 

The adoption of condition-based monitoring and real-time analysis can be done by operation managers and maintenance engineers to plan out maintenance schedules. Detect potential faults in the equipment before they lead to stoppages, thus reducing downtimes while boosting productivity.

Supply Chain and Logistics Leaders: 

Logistics and supply chain leaders can do real-time supply chain monitoring. Manage delivery schedules to gain the most effective route plans, manage disruptions, and ensure that your product gets to your clients on time.

Real-time analytics and streaming data are not restricted to a certain field and are the master key that opens a business up for growth. With raw data feeding into systems in real-time as the fourth industrial revolution rapidly unfolds, organizations that adopt this disruptive innovation will stand to benefit from the evolving business environment.

4. The Future of Real-time Analytics with Streaming Data

The integration of real-time analytics with AI and machine learning will provide a level of flexibility in the future of businesses that are unimagined.With these powerful combinations, businesses will be able to prevent, recover, and gain insights into processes, customers, and markets in real time.

In addition, the growth of the edge computing model suggests that data processing will occur in more localized settings, which will further reduce latency. This is especially true for industries such as manufacturing, where monitoring of production lines will be done in real time and can help avoid a range of expensive losses.

Real-time analytics is still a relatively young field, but as more and more organizations realize its potential, it can be expected that more diverse fields of business and industry will start utilizing it. Closely related, from third-party logistics providers seeking to improve the efficiency of delivery routes to banking institutions, hoping to identify suspect transactions, the possibilities are endless. The current trends of implementation and scaling point towards a future rich in new technologies and Business Intelligence (BI) mechanisms. This highlights the ongoing development driven by the increasing demand for real-time data analysis. Real-time analytics with streaming data is not something that businesses should just pursue as the latest trend; it is the proactive force that will radically alter the nature of business in years to come. Thanks to this technology and its updates, companies can achieve a competitive advantage and a sustainable development trajectory.

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Table of content
Introduction
1. Yellowfin
2. Sisense
3. QlikView
4. Kyligence
5. Tableau
Winding Up

Introduction

In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future. 

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise. 

In today’s exclusive AITech Park article, we will take a quick look at the top five augmented analytics tools that data scientist teams can depend on to democratize advanced-level analytics with augmented data ingestion, data preparation, analytics content, and DSML model development. 

1. Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance. 

2. Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed. 

3. QlikView

QlikView is well-known for its data visualization, analytics, and BI solution that aids IT organizations in making data-based strategic decisions with the help of sophisticated analytics and insights drawn from multiple data sources. The platform allows data scientists to develop, extend, and embed visual analytics in existing applications and portals while adhering to governance and security frameworks. However, some users have reported that the software may slow down when assembling large datasets. Additionally, the software sometimes lacks the desired feature and depends mostly on plugins from the older QlikView, which lacks compatibility with the updated Qlik Sense. The QlikView comes in three pricing plans: Standard Plan: $20/mo for 10 full users only, with up to 50GB/year data for analysis, Premium Plan: starts at $2,700/mo and 50GB/yr data for analysis and more advanced features and  Enterprise Plan: Custom pricing, starting at 500GB/yr data for analysis.

4. Kyligence

The fourth augmented analytics tool that data scientist teams use is Kyligence, as it stands out for its automated insights and NLP technology for businesses to generate deep insights within seconds. The technology also promises a centralized, low-code platform that emphasizes a metrics-driven approach to business decision-making and further identifies the ups and downs of the given metrics, along with discovering root causes and generating reports within seconds. However, the tools are considered to be quite complex and expensive when compared to other augmented analytics tools on the market. Kyligence comes in three pricing plans. Standard plan: $59/user/month, Premium plan: $49/user/month (minimum 5 users), and Enterprise+ plan: Flexible pricing and deployment options.

5. Tableau

Last but not least, we have the famous Tableau, an integrated BI and analytics solution that will help in acquiring, producing, and analyzing the company’s data and provide insightful information. Data scientists can use Tableau to collect information from a variety of sources, such as spreadsheets, SQL databases, Salesforce, and cloud applications. Talking about the interface, it is quite easy regardless of your technical skills, allowing you to explore and visualize data effortlessly, but professionals at an executive level might have issues adapting to this technology. However, the most concerning part of this software is its high pricing and lack of customization in terms of visualization options. In terms of pricing, Tableau comes with two exclusive plans: for an individual user, it is $75/month, and for two users, it is $150/month.

Winding Up

With the importance of data, data analytics, and augmented analytics tools, data scientists are paving the way for effortless and informed decision-making. The five tools listed above are designed to automate the complex data analysis process.

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Table of contents
Introduction
1. Getting Started with Platform Engineering
2. The Top Three Platform Engineering Tools You Should Consider in 2024
2.1. Crossplane
2.2. Port
2.3. ArgoCD
Conclusion

Introduction

Platform engineering is considered a practice built up on DevOps guides that assist in improving each development team’s compliance, costs, security, and business processes, eventually helping to improve developer experiences and self-service within a secure, governed framework. 

Lately, there has been quite a buzz about the permanent implementation of platform engineering in the IT industry. According to a recent report by Gartner, it is estimated that more than 80% of engineering organizations will have a crew dedicated to platform engineering by 2026, where these teams will focus on building an internal developer platform. This also implies that regardless of the business domain, these platforms by nature will help in achieving high business scale and reduce the time it takes to deliver business value. 

In today’s exclusive AI TechPark article, we will help IT developers understand the need for platform engineering along with the top three trending tools they can use for an easy business operation. 

1. Getting Started with Platform Engineering

Platform engineering is not for every company; for instance, in fledgling startups, where every individual does a bit of everything, this guide doesn’t come in handy. On the other hand, for companies that have two or more app teams where duplicate efforts are observed, platform engineering is the best option to tackle that toil, allowing the developers to think outside the box.

The best way to start the platform engineering journey in your organization is to have a conversation with the team of engineers, allowing them to understand and survey bottlenecks and developer frustrations, further advising the use of platform engineering that embeds and pairs programming within application teams.

During the process of building an application, developers need to question the size of the requirements, patterns, and trends needed in the app, the bottlenecks, and many more. However, it doesn’t end here, as to further comprehend the application, they require multiple testing and opinion polls by their internal customers; developers are also required to document every minute detail and change on the platform to encourage self-service and independence in the long run. 

Therefore, whether it is infrastructure provisioning, code pipelines, monitoring, or container management, the self-service platform will be a guide to hiding these complexities and providing developers with the necessary tools and applications. 

2. The Top Three Platform Engineering Tools You Should Consider in 2024

In this section, we will introduce you to the top three tools that every platform engineer should try in 2024 to perform routine tasks without being time-consuming and with zero human errors. 

2.1. Crossplane

Navigating the intricate landscape of Kubernetes infrastructure, Crossplane is one of the best platform engineering tools that securely builds a control plane with its tailored and unique needs without writing tricky distributed systems code. Crossplane is a master orchestrator that extends beyond container management, as its reliability and security are inherent to Kubernetes. 

2.2. Port

Port emerges as an indispensable asset of platform engineering, offering DevOps teams a centralized platform for orchestrating applications and infrastructure with unparalleled precision and control. The platform has a unique blend of oversight and flexibility that allows IT managers to maintain standards and best practices to streamline the business process effectively and efficiently. 

2.3. ArgoCD

Argo CD, a Kubernetes-native marvel, has redefined the landscape of modern application deployment. It offers a meticulous orchestration of deployment processes, ensuring that the applications are not just deployed but thriving and in sync with the demands of the tech world. The platform empowers developers to take full command, seamlessly managing both the intricate web of infrastructure configurations and the pulsating lifeline of application updates, all within a single, unified system.

Conclusion

Platform engineering is considered the optimal suite of tools that aids in orchestrating a symphony of tools that align with developers’ unique operational needs and aspirations while also keeping cost, skillset compatibility, feature sets, and user interface design in consideration.

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Introduction

Cloud computing has opened new doors for business applications and programs to utilize databases to store data every day worldwide. These databases are well-known for securing data and making it accessible only to channels where the chief data officer (CDO) permits. Previously, organizations depended on database-paid suites, which were expensive and limited in options; however, now IT organizations have open-source databases for all their data, as these are affordable and flexible. However, it is often difficult to find the right cloud database service provider that will not only store the data of your company but also transfer it to the database, while data professionals can access it anywhere with an internet connection.

In this review article by AITech Park, we will explore the top five open-source cloud databases that can be used by IT professionals to build robust applications.

1. SQLite

SQLite is recognized as one of the most lightweight embedded relational database management systems (RDBMS) that operate inside applications. To power this embedded database, SQLite has a fully functional application that works as a library that supports ACID transactions. The software has an embedded library that has an SQL database engine supporting ACID transactions, which further reads and writes data through tables, indices, triggers, and views that can be contained in a single file. With a recent update on SQLite, data professionals and developers can use this software in the form of mobile applications, web browsers, and IoT devices, allowing smaller digital footprints and less load on the software.

2. MariaDB

MariaDB is considered one of the clones of MySQL as it was built on the same code; however, over the years, it has developed to be user-friendly for executive-level data professionals. With newer updates, MariaDB operates on the Aria storage engine to conduct complex SQL queries, ultimately giving it a speed boost over MySQL. The most unique feature of this open-source database is that it allows pluggable storage engines, allowing data teams to go beyond normal transactional processing. For instance, teams can use ColumStore for high-volume data storage and distribution. The ColumnStore can also be used for columnar analytics and hybrid smart transactions (HTAP), which improve data replication and support many JSON functions.

3. Apache CouchDB

CouchDB by Apache is a database duplication tool that deters data loss in the event of network failure or any other pipeline failure. The software creates a dedicated database system that can operate efficiently on ordinary hardware, not just by deploying on one server node but also as a single analytical system across numerous nodes in a cluster, which can be mounted as needed by adding more servers. For a seamless operation, the database uses JSON documents to store data and JavaScript as its query language. Further, it also supports MVCC and the ACID properties in individual documents.

4. MySQL

MySQL is one of the most popular and oldest open-source databases, and it is known as its best database for web-based apps such as Trello and Gmail. The database software uses the Structured Query Language (SQL), which lets data professionals store data in tables, develop indexes on the data, and query the data. MySQL supports an expansive variety of techniques and has a very low probability of getting the data corrupted as it gears for transactional uses, further supporting analytics and machine learning (ML) applications.

5. PostgreSQL

PostgreSQL became popular among data professionals and developers around 1995 when it started working as a SQL language interpreter, and decades later it became a popular open-source cloud database. This database software offers full RDBMS features, such as ACID compliance, SQL querying, and clearance for procedural language queries to develop stored procedures and stimuli in databases. PostgreSQL also supports enterprise applications that demand complex transactions and high levels of concurrency, and occasionally for data warehousing. It also supports multi-version concurrency control (MVCC), so data can be read and edited by various users at the same time, and it also sustains other varieties of database objects.

Conclusion

To create any kind of app, developers and data professionals need a secured database where they can save files and confidential data required for numerous use cases. While we are well aware that closed databases are expensive and use licensed codes, the above open-source database software provides data engineers with the flexibility to build their own DBMS without breaking the bank.

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Introduction

Product design is a dynamic field with an ever-evolving concept, especially with the introduction of AI and generative AI being one of the most transformative focuses in reshaping the product design landscape. The force behind creativity is the product designers who play an essential role in merging technology and design solutions to bring out advancements in products we interact with every day.

The AI tools and software help product designers test different design iterations allowing them to focus on collaborating with cross-functional teams and solving complex design challenges. These tools are built on advanced technology tool that integrate the intelligence of AI and machine learning (ML) algorithms into visual design functions. It allows product designers to construct more precise and personalized visuals quickly and more efficiently.

Therefore to ease your search for the best AI tools AI TechPark brings you a compilation of the best AI design software and tools of 2024 that will help you in designing products efficiently.

1. Uizard Autodesigner

Uizard Autodesigner will be a useful AI tool for  any digital product and service designer as this software allows you to generate editable UI designs from written/ text prompts. This product designing tool generates multi-screen product designers for both mobile applications and web design, allowing you to choose a visual style and themes according to business requirements.

2.  NVIDIA Omniverse

One of the most recognized product designing software among product designers is NVIDIA Omniverse. This software has built 3D graphics and computing platforms. It integrates 3D design, spatial computing, and physics-based workflows across third-party apps and AI services. Product and industrial designers rely heavily on 3D rendering models. This is where NVIDIA Omniverse comes in handy to help them visualize the product design with ease. It delivers stunning visuals bringing ideas to life.

3. Adobe Firefly

Adobe Firefly is powered by Adobe Sensei AI, which is integrated with the Creative Cloud suite in Photoshop and Illustrator, which helps in manipulating specific sections in a photograph. Therefore product designers believed that Adobe Firefly is one of the best AI image-generation tools that will help in enhancing your natural creativity as this platform provides numerous useful tools such as text to image, generative fill, text effects, and recolors.

4. Framer AI

For any newbie product designer who is looking for a user-friendly interface, to streamline their design process Framer AI is your go-to tool. Just by text prompting your ideas, this tool will help you to generate and publish new website designs with AI within seconds. Framer AI also allows you to then rearrange across the various color palettes, and fonts and further offers AI tools to render content for your web layout.

In the end

With numerous AI design software popping up everyday it can be a challenging task to figure out the right tools. These tools help in harnessing the power of AI to automate time-consuming tasks, understand user preferences, and make suggestions based on those preferences. Especially with these AI tools at your disposal you can create numerous creative and innovative product designs.

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Introduction 

In the evolving landscape of data management, age-old approaches are gradually being outpaced to match the demands of modern organizations. Enter as a savior: Data Mesh, a revolutionary concept that modern organizations harness to reshape their business models and implement “data-driven decisions.” Therefore, understanding and implementing Data Mesh principles is essential for IT professionals steering this transformative journey. 

At its core, data mesh is not just a technology but a strategic framework that addresses the complexities of managing data at scale, as it proposes a decentralized approach where ownership and responsibility for data are distributed across numerous domains. 

This shift enables each domain or department to manage data pipelines, maintain and develop new data models, and perform analytics across all interconnected integrations to facilitate infrastructure and tools that empower domain teams to manage their data assets independently. 

At the core of the data mesh architecture lies a robust domain team that is the powerhouse behind the creation, delivery, and management of data products. This team comprises professionals with domain-specific knowledge who will epitomize the decentralized nature of data mesh to foster greater ownership, accountability, and agility within the organization. 

This AITech Park article will explore how to build a data mesh team by outlining roles and responsibilities to drive success in an organization. 

1. Data Product Owner (DPO)

The DPO, or Data Product Manager, is an emerging role in the field of data science that manages the roadmap, attributes, and importance of the data products within their domain. The DPO understands the use cases in their domain to serve as per UX and gets acquainted with the unbounded nature of data use cases to create combinations with other data in numerous forms, some of which are unforeseen.

2. Data Governance Board

After infrastructure, the data governance board is a critical part of the data mesh as they oversee the enforcement of data governance policies and standards across data domains. The board represents data product managers, platform engineers, security, legal, and compliance experts, along with other relevant stakeholders, who will tackle data governance-related problems and make decisions across the various domains within the business. 

3. Data Stewards

In creating a robust data mesh domain, data stewards play a critical role when it comes to keeping the data catalog. Data stewards ensure that their domain data is of high quality, but they also operate across the field to spot and find accurate data quality to maintain data reliability. They also help to maintain metadata and collaborate with others across domains, so their data sets are accessible and easy to understand. 

4. Data Platform Owner

In a data mesh, one of the purposes of the platform is to facilitate domains to build and share data autonomously. Therefore, the role of the data platform owner is to develop an infrastructure that supports the growth, deployment, and ongoing maintenance of data products. They create data catalogs that will provide clarity about data definitions, lineage, and other business attributes, and they can comprehend and leverage their data as an asset. 

Conclusion 

Building and maintaining a data mesh team needs careful planning, strategies, and commitments to develop talents across all boards. Therefore, organizations must adopt a hybrid organizational structure so that they can establish roles and responsibilities that help drive innovation, agility, and value creation in the digital age.

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Table of Content
Introduction
1. Auto-SKLearn
2. Google AutoML Cloud
3. Auto-Keras
4. TransmogrifAI
Conclusion

Introduction

With the rapid growth in the digital world, organizations are implementing Automated Machine Learning (AutoML) that helps data scientists and MLOps teams automate the training, tuning, and deployment of machine learning (ML) models. This technology will save time and resources for the data scientists and MLOps teams, which will accelerate research on ML and solve specific problems related to ML models. 

For instance, some AutoML tools focus on optimizing ML models for a given dataset, while others focus on finding the best model for specific tasks, such as picking the appropriate ML algorithm for a given situation, preprocessing the data, and optimizing the model’s hyperparameters, aiding different industries to predict customer behavior, detect fraud, and improve supply chain efficiency. 

Therefore, AutoML is a powerful mechanism that makes ML models more accessible and efficient; however, to create a model, execute stratified cross-validation, and evaluate classification metrics, data scientists and MLOps teams need the right set of AutoML tools or platforms. 

In today’s AI TechPark article, we will introduce you to the top four AutoML tools and platforms that simplify using ML algorithms.

1. Auto-SKLearn

Auto-SKLearn is an AutoML toolkit that is available as an open-source software library that can automate the process of developing and selecting the correct ML models using the Python programming language. The software package includes attributes that are used in engineering methods such as One-Hot, digital feature standardization, and PCA. It improvises the model and operates SKLearn estimators to process classification and regression problems. Furthermore, Auto-SKLearn builds a pipeline and utilizes Bayes search to optimize that channel, adding two components for hyper-parameter tuning using Bayesian reasoning: The tools also have an inbuilt meta-learning feature that is used to format optimizers using Bayes and assess the auto-collection structure of the arrangement during the optimization process.

2. Google AutoML Cloud

The Google Cloud AutoML suite is designed to make it easiest for data scientists and MLops teams to apply ML-specific tasks such as image and speech recognition, natural language processing, and language translation in business. The platform accelerates the process of building custom AI solutions with a variety of open-source tools and proprietary technology that Google has evolved over the last decade. AutoML supports homegrown TensorFlow and offers partially pre-trained features for designing custom solutions using smaller data sets.

3. Auto-Keras

Auto-Keras is an open-source software library for AutoML developed by DATA Lab and helps data scientists create a deep learning (DL) framework. The platform provides processes to automatically search for the architecture and hyper-parameters of DL models. Auto-Keras offers superior-level APIs such as TextClassifier and ImageClassifier that solve any ML problem with just a few codes. For instance, Auto-Keras simplifies the ML models by using automatic Neural Architecture Search (NAS) algorithms; these NAS algorithms automatically adjust models to replace DL engineers.

4. TransmogrifAI

The most famous open-source library in AutoML is TransmogrifAI, which is built on Scala and SparkML, aiding data scientists to rapidly produce data-efficient models for heterogeneous structured data on a large scale. With a few codes, data professionals could easily automate the data cleansing process, use feature engineering in designing new ML models, and select the right model to further explore and iterate the datasets.

Conclusion 

In this competitive economy, organizations are looking for AI, ML, and DL solutions that will help them transform big data into actionable insights, reach a target audience, improve decision-making, and streamline business processes. However, the whole process of implementing these solutions can be automated with the help of the above AutoML platforms. These AutoML platforms can automate repetitive tasks for data scientists and MLops teams, allowing them to spend more time-solving other business problems.

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