Big Data Pipeline Architecture and its Benefits

Big Data Pipeline Architecture and its Benefits

Introduction to Big Data Pipeline

What is Data Pipeline? 

A data pipeline moves data from the source to a destination such as a data warehouse, data lake, and data lakehouse. Along the pipeline, data is transformed and optimized, thus making it easier to be analyzed and develop business insights.

What is Big Data?

You might have heard about the term “Big Data.” Big data is not big without data variations, data volume, and velocity of data. The data can be of any format, of any size and any type. If it satisfies, then there would be no hesitation to call that data as Big Data. Big Data is now the need of almost every organization as data is generated in large volumes and these large volumes contain data of every known or unknown type/format. Big Data creates problems like handling data, manipulating data and Analytics for generating reports, business, etc. There comes a solution too as Every problem is a solution. This solution is the development of Data Pipeline.

What is Big Data Pipeline?

Big Data helps to produce solutions like Warehouse, Analytics, and Pipelines. Data Pipeline is a methodology that separates compute from storage. In other words, Pipeline is commonplace for everything related to data whether to ingest data, store data or to analyze that data.

Let us assume a case that you have many works such as Data Analytics, Machine learning, etc. Are in line up and store for that data is shared. In this case, we can Ingest data from many resources and store it in their raw format at Data Storage layer. Now, It will be easy to perform any work from this data. We can also transform that data into data warehouses.

What is the difference between Big Data Pipeline and ETL?

Sometimes, people get confused by two terms as some use cases use both As keywords interchangeably. But Both are, in fact, different as ETL (Extraction, Transformation and Load) is a subset of Data Pipeline Processing.

• ETL is usually performed on Batches (here batch processing)
• Data Pipeline contains both Batch and Real-Time Processing as Batch Engine and Real Time data processing Layer

Following steps are followed for Building Big Data pipeline -

• Data sources are defined and connected via connectors
• Data is ingested in its Raw form
• Data is then processed or Transformed
• Data resides to Warehouses
• Data can be used for Machine Learning, Reporting, Analytics and so on

There are some critical points that everyone must consider before making a Data pipeline.If appropriately followed one can effectively use those economic or data resources. These points are -

• If data is critical than it is recommended not to use cloud storage. One has to invest in building up fresh storage of their own.
• Completely mark the line between job scheduling for Real-time and Batch data processing.
• Openly exposing SSL keys is not recommended, try to keep them as Secure as possible as these might expose data to attackers.
• Build Pipeline for suitable workload as these can be scale in and out. So implementing future tasks in the present workload is not at all efficient use case.

What are the benefits of Big Data Pipeline?

• Big data pipelines help in Better Event framework Designing
• Data persistence maintained
• Ease of Scalability at the coding end
• Workflow management as the pipeline is Automated and has scalability factors
• Provides Serialization framework

There are some disadvantages of data pipelines also, but these are not that much to worry on. They have some alternatives ways to manage.

• Economic resources may affect the performance as Data Pipelines are best suited for large data sets only.
• Maintenance of job processing units or we can say Cloud Management.
• No more privacy on the cloud for critical data.

What is Big Data Pipeline Automation?

Data Pipeline Automation, helps to automate the various processes such as data extraction, transformation and integration before sending into data warehouse.

What are the best data pipeline tools?

• Apache Spark
• Hevo Data
• Keboola
• Astera Centerprise
• Etleap

Why do we need Big Data Pipeline?

Data Pipelines reduces risk of same place capturing and analyzing impairing of data as data is achieved at a different location and examine at a different location.

• It maintains dimensional of the system for various visualization points of view.
• Data Pipelines helps in Automated Processing as Job scheduling can be managed and Real Time data tracing is also manageable.
• Data Pipeline defines proper task flow from location to location, work to work and job to job.
• These have Fault Tolerance, inter-task dependencies feature, and failure notification system.

What are the requirements for Big Data Pipeline?

When we talk about running anything in a computer system, there are always some requirements for the same. Big Data Pipelines also has some requirements, such as:
 

• Messaging Component(s) like Apache Kafka, Pulsar, etc. must be defined
• Store (no limits storage) for storing data files of large sizes in Raw format
• Unlimited Bandwidth for transmission
• Additional Processing Units or Cloud (Fully Managed or Managed)

What are the use cases of Big Data Pipelines?

Most of the time, every use case describes how it is essential and how they are implementing it. But why is it necessary too? There are some why points for some of the use cases for Public organizations.

• Consider Forecasting system where data is the core for the financing and Marketing team. Now, Why do they use Pipeline? They can use it for Data aggregation purposes for managing product usage and reporting back to customers.
• Imagine a company using Ad marketing, BI tools, Automation strategies, and CRM. Here, Data is necessary to manage and collect for occasional purposes now if a company relies on these tasks individually and wants to upgrade its workflow.
  They have to merge all work under one place, and here Data pipeline can solve their problem and help them build a strategic way to work.
• Imagine a company that works on crowdsourcing. It is obvious that they are using many different data sources for crowdsourcing and performing some analytics on that data. So, to obtain better output from crowdsourcing in near real-time and for analytics and ML, that company should build a data pipeline to collect data from many sources and use it for their purposes.

A Data Driven Approach

Data Pipeline is needed in every use case that one can think of in contrast to big data. From reporting to real-time tracing to ML to anything, data pipelines can be developed and managed for these problems.For making strategic decisions based on data analysis and interpretation we advise taking the following steps -

• Know more About Solution for Building Big Data Pipeline on AWS
• Learn how to create Big Data Platform

Original article source at: https://www.xenonstack.com/

#bigdata #architecture #benefits 

Learn to Model-Centric AI Benefits for Enterprises

Overview of Model-Centric AI

To achieve a good AI solution, a careful balance between Model-Centric a Data-Centric perspective is fundamental. AI System = (Model Centric AI) Code + Data (Data-Centric AI).

The model-centric approach requires experimental research to increase the performance of the ml model. This involves choosing the appropriate model architecture and training technique from various options. Organizations can maintain their data while improving the code or model design in this manner. The primary goal of this method is to work on code. Currently, most AI applications are model-centric. One possible reason is that the AI ​​industry is paying great attention to academic research on models. More than 90% of the research literature in this field focuses on models. Indeed, it isn't easy to generate large datasets that can become a widely accepted standards. Therefore, the AI ​​community believes that model-focused machine learning holds more promise.

Model-centric Approach to AI

A model-centric approach to AI focuses on creating high-quality models using suitable machine-learning algorithms, programming languages, and AI platforms. This approach has significantly advanced machine learning/deep learning algorithms.

The focus on building powerful models has spawned many AI/machine learning/deep learning frameworks using various programming languages ​​such as Python, R, etc.

These popular frameworks include Python Sklearn, Tensorflow, Pytorch, and others. Besides, almost all cloud service providers are developing AI/ML services focused on building machine learning models.

Key Points

• Improve the model while keeping the data the same.
• Optimize model performance.
• Keep data static through the model development workflow process.

Why is it important?

In ML project life, Model Centric AI can improve the model (or “code”) and the performance rather than the data to make high-quality results through all the stages.

Industries like as:-

1. Media and advertising
2. Healthcare
3. Manufacturing

A manufacturing company with several products cannot employ a single machine learning system to identify production errors across all of its products, in contrast to the media and advertising businesses. Instead, every manufactured product would need a uniquely trained ML model.

While media firms can afford to have a whole ML department working on every little optimization challenge, a manufacturing company that needs several ML solutions cannot fit into such a size template.

What are the benefits of Model-Centric AI?

The following are some advantages of this approach:-

1. The most common AI challenges may be handled by using methods.
2. Improvements in AI technology result from the emphasis on creating better models.
3. Organizations with data and wish to apply AI to address business challenges can employ this strategy.
4. ML is an iterative process that involves designing empirical tests around a model to improve performance.
5. It involves finding the exemplary model architecture and training process in many possibilities to achieve a better solution.
6. Model-centric AI collects all the data you can collect and develop a good enough model to handle noise in the data.
7. The established process requires keeping the data constant and improving the model over and over until the desired result is achieved.

Difference Between Model-centric AI and Data-centric AI

Model-centric AI and data-centric AI are two different approaches to AI.

• A model-centric approach focuses on creating high-quality machine-learning models using suitable algorithms, programming languages, and AI platforms. This approach has significantly advanced machine learning/deep learning algorithms. In contrast, a data-centric approach to AI is focused on getting the correct data that can be used to build high-quality, powerful machine learning models.
• Unlike model-centric AI, the focus shifts to obtaining high-quality data to train the model rather than the model. There are many different approaches to AI, but a hybrid or balanced approach that employs model-centric and data-centric models is the most effective way to design intelligent machines.

What are the key features of model-centric AI?

The key features for model-centric AI is listed below:

Data manipulation is a one-time operation

Traditionally, data science teams download static data from a source. It is then processed, cleaned, and stored in the database. After that, it got little attention because data collection and processing was considered a one-time thing.

Quantity of data outweighs data quality

This approach dramatically emphasizes the amount of data to make the models work. So even though we have a lot of data to work with, the benefits could be more commensurate with the effort.

Focus on the model

The main focus is on algorithms and models to improve the predictive performance of AI models. Various algorithms are tested, and the hyperparameter is gradually adjusted to achieve the required improvement.

Future Trends

AI systems are made up of code, and data code, which refers to the model created using frameworks in languages like Python, C++, R, etc. All research labs worldwide aim to develop a model architecture that would perform better and advance the state-of-the-art for a specific dataset, such as the COCO dataset. It is known as a "model-centric strategy" to maintain the data constantly while tweaking the model's settings to enhance performance.

This is beneficial for ML engineers to access updated and improved models efficiently and to develop the best model possible for ML projects. The unique feature of this period is that data gathering was a one-time effort carried out at the beginning of the project to grow the dataset over time but with little consideration of its internal quality. The concept was developed for small-scale deployments when a single server or device could manage the whole load, and monitoring wasn't an issue. The main challenge was that everything had to be done manually, including data cleaning, model training, validation, deployment, storage, and sharing.

Conclusion

A data-centric approach to AI aims to collect the correct kind of data that can be utilized to create machine learning models of the highest magnitude and performance. This approach is expensive as it requires a lot of data to train the models. In contrast, in model-centric AI, the emphasis now turns to obtaining high-quality data for training models.

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Original article source at: https://www.xenonstack.com/

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Intro to Advanced Data Discovery Benefits and its Features

Introduction to Advance Data Discovery

Advanced data discovery is not limited to data scientists or IT staff only in today's world. Even business users also demand it. Business users demand quick and easy preparation and analysis data, visualize and explore data, notate and highlight the data, and share the data with others to identify the important nuggets. Without advanced analytics, it is impossible to achieve this within seconds. But with the concept of advanced data discovery allows business users to leverage advanced analytics, which helps in the rapid return of investment, increases revenue, and lowers the total cost of ownership. The key to data democratization and data literacy is Augmented Analytics. When an advanced analytics application for enterprise customers is developed, it encourages team members to use advanced analytics and lets the organization grow Citizen Data Scientists.

What is Advanced Data Discovery?

• Advanced-Data Exploration helps enterprise users effectively prepare and view, analyze and discover information, note, highlight, and share data with others.
• Market users may use Sophisticated Data Analysis to discover the critical 'nuggets' hidden in traditional data, link the dots, detect exceptions, recognize patterns and trends, and help forecast performance.
• The best-advanced data discovery platform is intended for enterprise users with average skills to do all of this without technical experience, knowledge of mathematical science, or assistance from IT or trained data scientists.
• A platform for data exploration is a critical tool for any enterprise customer in your organization. With so many data sources, the consumers can’t know whether they have access to complete, accurate data for their organization to make decisions in so many places.

Importance of Analytics for Advanced Data Discovery

• With the correct Advanced Analytics Software, market users can access data integrated from multiple data sources. They can use the data for Advanced Data Discovery to gain insight into problems and opportunities, share information with other users, and be more efficient, motivated, and accountable.
• Advanced Analytics requires the detection, interpretation, and communication of meaningful patterns of information and considers and applies trends and patterns to make clear, fact-based choices.
• In other words, advanced analytics connects information to actions and strategies and allows the organization to set targets and objectives that are practical and feasible in terms of competition.
• Although in the past few years, businesses have turned to IT and data analysts to identify, evaluate and understand data.
• Today the business market is evolving too fast to wait for this information, but the truth is that business consumers need this information and expertise to do their job.

How Advanced-Data Discovery helps the organization in achieving its goals?

• Concepts such as Advanced Data Discovery and Augmented Analytics can seem elusive and daunting to the average enterprise. Nothing more from the truth can be there! The solutions available today for Advanced Analytics Applications are diverse and flexible. The right intelligent technology exploration strategy will promote data democratization, social BI, and enthusiastic consumer acceptance around the organization at every stage of the business.
• The required Advanced Data Discovery helps business users leverage complex analytics in an elegant, easy-to-use environment. It turns business users with average technical expertise into Citizen Data Scientists.
• These data exploration tools deliver precise, concise results that allow the enterprise in any division and place to rapidly and easily prepare and analyze information and model and explore it, notice and highlight data, and exchange data around the company.
• Advanced analysis of data is not out of reach for our squad. The correct Advanced Analytics Platform helps any consumer perform research without technical experience, knowledge of predictive analysis, or assistance from IT or professional data scientists.

What are the features of Advanced Data Discovery?

The common features of Advanced Data Discovery are listed below:

Preparation of Self-Serve Data

Enables enterprise users to perform sophisticated data analysis and auto-suggest partnerships, demonstrates the importance and significance of critical variables, proposes data type casts, data consistency changes, and more.

Smart Visualization

Smart Data Visualization proposes the best choices for a given array or class of data to be visualized and plotted based on the nature, dimension, and form of data.

Predictive Analysis Plug n 'Play

Supported predictive modeling and predictive algorithms (associative, decision trees, sorting, clustering, and other techniques) allow market users to use Sophisticated Data Exploration and early prototyping recommendations to explore hypotheses and conclusions and minimize computational and experimental time and cost significantly. It empowers market customers with access to meaningful data to test theories and concepts without the aid of data scientists or IT staff.

Conclusion

It's quick to grasp the benefits of auto-suggestion and auto-recommendation. In the past few years, suppose market customers can use methods that complement average capabilities without needing advanced technical or analytical experience and information. In that case, they are more likely to use these services to obtain practical insight and make confident judgments and predictions.

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Original article source at: https://www.xenonstack.com/

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Learn Customer intelligence Benefits and Its Use Cases

Introduction to Customer Intelligence

Businesses can not exist without their customers. The customers are essential for every business as they bring revenues. Every business is in the race to attract more customers than other businesses either by lowering the prices of their products or services, providing offers, advertising, or developing unique and loved products.
Every person is a customer of one business or another. If anybody has a bad experience with a company, they may lose trust in the company and lose its customer.

Businesses need to understand their customers and engage them. It helps the businesses to acquire new customers and keep the old ones. Happy customers are more likely to repeat business with the companies that fulfill their needs and expectations and provide good services.

What are the challenges of Customer Intelligence?

Market changes very rapidly, and it is the need of the hour for companies to convert and retain loyal customers. A company needs to understand its customer’s interests and preferences. The main challenges that a company faces while understanding their customers are:

• How does a company know what its customers want?
• How does a company provide the best services and products to its customers?

It explores how the customers interact with the company and its website. The company must track its customer’s purchase history, behavior, time spent on particular pages to get an idea of improving their products and services.

Customer Intelligence in Business and Marketing

Customer intelligence is the analysis and collection of customer data to understand the customer needs and interests, provide the best services and make informed decisions.

In every sector, businesses can benefit from customer intelligence. The more a company knows its customers, the better it can interact with them. It allows the companies better to understand their customers’ preferences, motivations, patterns, wants, needs by combining demographic data, transactions, second-and third-party data, channel activity, and sales and marketing history. It also enables the companies to build more profound and more effective customer relationships. It is becoming a critical ingredient in making effective strategic decisions, and it’s the foundation of building future business intelligence capabilities.

Customer intelligence collects data from multiple sources and uses artificial intelligence, machine learning, business intelligence, data visualization, and predictive analytics. It helps the business develop insights around hyper-segmentation, personalization, next best action, and forecasting. These insights lead to reduced customer churn and improved customer experiences.

What is the use of Customer Intelligence?

Fig 2: Uses of Customer Intelligence

Behavioral Segmentation

Behavioral segmentation divides the whole population into segments based on the same pattern followed by customers. Customers may have the same previously purchased products, similar reactions to messages, and similar feedback.

Geo-Targeting

Apps like online food delivery use customers' locations to offer the closest restaurant to the customer. It is the easiest and effective way to customize messaging and offers.

Personalization

The company will do personalized messaging and provide offers accordingly based on the customers' behavioral segments, known preferences, or buying patterns.

Modeling User Flows

User Flow is a path the user takes on a website or an app while completing the task. Businesses can monitor users’ movements through their journey with the help of customer intelligence and enable businesses to model user flows on-site and identify improvements to optimize the user flows. For example, when a person arrives at an online store, the products he searches, products added to the cart, and finally, the purchase is a user flow.

What are the benefits of Customer Intelligence?

Using customer intelligence will benefit the company from any sector. Some of the advantages of customer intelligence are:

• Data-driven decisions: Collecting and analyzing customer data in detail will help the companies make data-informed decisions. These decisions will lead the company to take steps that will benefit its customers the most.
• Personalized Marketing: A customer intelligence system enables highly personalized customer interactions.
• Customer Satisfaction: The personalized interaction achieved from customer intelligence will help in better customer satisfaction, which helps to increase the Net Promoter Score and other attributes.
• Customer Retention: Customer intelligence will help reduce the organization's customer retention challenges.
• Keeping up with Market Changes: E-commerce and retail industries are changing very fast. It is not affordable for any company to be behind the market. Customer intelligence will make a company aware of the latest trends and people's interests.

A good customer intelligence approach will give an organization a clear view of its marketing efforts. It focuses on the customer journey, which can help the company keep track of marketing activities bringing in better communication.

What is the Intelligent Approach to Customer Intelligence?

An intelligent approach is needed to achieve customer intelligence.

Data Collection

The first step in the customer intelligence process is to collect data. Various types of data are collected for customer intelligence.

• Demographic: The company can collect demographic data from surveys, statistics, records, and accounts, which will give information about who the customer is.
• Psychographic: The psychographic data is needed to know the customer’s personality and attitude. This data type can be collected from customer interviews, reviews, questionnaires, and surveys.
• Behavioral: This data will give customers how they behave when they interact with its products and services. This data can be collected from the company’s website by monitoring the customer’s activity, comments, and mobile browsing.
• Transactional: The data describes how the customer spends on the company’s products and services. It can be collected from payment methods, transactions data, order information, etc.

Evaluate the data / Analyze the data

The next step in the customer intelligence process is to analyze the collected customer data. Businesses can use various analytics tools to analyze the data and segment their customers based on their behavior and feedback. The companies can also pick up metrics that matter to their business and give a 360-degree view of their customers.

Share Insights

After analyzing the data, the next step is to share the insights obtained with the organization. It can be achieved using dashboards, reports, and customer journey maps.

Customer Intelligence By Customer Journey Mapping

This will help the companies to understand how, where, and when the customers have experienced the brand, creating a proper channel for customer intelligence through data collection and communication.

To achieve a successful customer experience, the company needs to measure the customer’s perception of the company from time to time. Businesses use some platforms to gather insights from customer journey mapping.

• Physical Location: When a customer comes to the store, restaurant or hotel, etc., the company can collect feedback from customers at the location itself.
• Emails: It is the easiest way to collect feedback from customers. Whenever a customer completes a purchase, the system automatically sends a message to give feedback.
• Website: If the company has an online retail store and customers visit the website more often, they can communicate with their customers and gather feedback from the website.

Use Cases of Customer Intelligence

The below highlighted are the Use Cases of Customer Intelligence

Financial Services

Customer Intelligence helps a bank to:

• Identify patterns
• Identify any unusual suspicious activity
• Determine the risks such as bad debt or fraud depending on the information about the customer.
• Predict Churning.
• It helps banks limit their offers to only those likely to leave and make cost-effective decisions.

Retail

Personalized Discounts

Retailers can reward customers for their loyalty using customer intelligence. They install sensors all around the store, and these sensors will send messages via email or app notification on their smartphones when the customer is near a particular product. One condition for the reward is that the customer must opt for themselves in the loyalty program.

Also, retailers can make online driven offlines sales from websites. Retailers can track what customers are looking for from their website and when the next time a customer enters the store, the retailer will send a personalized discount for the product.

Conclusion

Adopting technologies for providing improved customer experiences is a key to making more profits and customer retention in an organization. If the company wants to stand out from the competition, it should start using customer intelligence seriously to make informed data-driven decisions.
The insights organization will get from customer intelligence will increase brand loyalty and make the business ready to face any change in the industry.

• Know here How to enable Artificial Intelligence in Retail?
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Original article source at: https://www.xenonstack.com/

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Requirements Elicitation Processes and its Benefits

Introduction to Requirements Elicitation

Elicitation? It must be an easy thing!

Let me try. Okay, so first, I have to do what? Wait, this isn't that easy…

Exactly! It looks like elicitation is easy, but it's not, so let's understand first what requirement elicitation is in Project management.

Many processes and techniques can be used to manage projects effectively in the software development world. One of the most challenging tasks in any project is eliciting requirements. With so many different ways to go about it, it's easy to get lost and end up with a document that's nothing more than fragmented ideas instead of precise specifications. Eliciting requirements is an art and not a science. It requires you to use your instincts and engage with the client on a different level. Having said that, you can use various techniques and methodologies as a software engineer or project manager to streamline the process of getting useful information from your stakeholders. With so many software development methodologies available in the market today, it can take time for new entrants to choose which will work best for their specific needs.

What is Requirements Elicitation?

The first step in the software development process is requirements engineering. This is turning a business or organizational problem into a set of requirements for a new software application. This step identifies a problem, and a set of requirements is developed. It will include information about the problem, the stakeholders, their needs, and the reason for building the application. The problem statement is the primary input for this process.

The crucial part is that we need to gather information, not only information but also the correct information. Connecting with Stakeholders to understand precisely what they are looking for.

What are the benefits and importance of Requirements Elicitation?

• Requirements Analysis: It helps identify and understand the problem to be solved.
  It helps identify user needs and the problems that need to be solved. It is the first step toward designing and developing a solution.
• Requirements Documentation: Requirements documentation is necessary to make sure that all stakeholders are on the same page and agree on what is required to be done, how it will be done, and what technology will be used to deliver the results.
• Requirements Traceability: It ensures that each requirement can be traced back to its owner and links to the problem statement and other related requirements. This is important when changes need to be made to the requirements.
• Requirements Prioritization: This is where the business and domain experts will come together to determine which requirements are most important and need to be implemented first.
• A good understanding of the problem will help you to deliver a solution that is more likely to be successful.
• Clear and well-documented requirements will help stakeholders and team members stay on the same page and agree on what needs to be done.
• Documented requirements will also help minimize the number of defects in the resulting product.
• A proper requirements engineering process can help the organization save money and increase productivity.
• A proper requirements engineering process will help you be more successful in the job hunt because it will showcase your ability to understand the problem and deliver a solution to meet the stakeholders' requirements.
• It can help you to decide which software development methodologies will work best for your specific needs.

What are the best ways to do Requirements Elicitation?

• Build Rapport: This is the first step toward eliciting requirements. Building rapport with your stakeholders will help them feel more comfortable sharing their problems and needs, making them more likely to respond to your questions and suggestions.
• Ask Open-Ended Questions: Closed-ended questions are more likely to result in "yes" or "no" answers. These are not very helpful when you are trying to get a good grasp on the problem and get a good set of requirements.
• Write Down Everything: This is an essential part of the process. You will want to write down everything your stakeholders say, even if it sounds silly or unrelated. You can sort through the information and determine what is useful and what isn't once you have finished asking questions.
• Get as Many Stakeholders as Possible: It's not enough to talk to just one person. You will want to talk to as many stakeholders as possible to get a well-rounded picture of the problem and a good set of requirements.
• Take Your Time: You don't want to be in too much of a rush. This is not a quick process. It will take time to meet with stakeholders, ask questions, and get a good grasp of the problem and the requirements.

What are the processes of Requirements Elicitation?

The significant steps which should be involved in this procedure are –

1. We need to identify all the stakeholders, for example. Users, developers, customers, etc.
2. Always list out all the requirements of customers.
3. A value indicating the degree of importance must be assigned to each requirement.
4. At last, the final list of requirements must be categorized as follows:
5. What is possible to achieve

• What should be deferred and the reason for it
• What is impossible to achieve and should be dropped off.

Conclusion

Requirements engineering is turning a business or organizational problem into a set of it for a new software application. The first step towards designing and developing a solution is requirements engineering. It is essential to understand the problem statement and the user's needs. It helps in identifying and understanding the problem to be solved. It also helps identify user needs and the problems that need to be solved. It is the first step toward designing and developing a solution. It is essential to document requirements so stakeholders are on a similar page and agree on what needs to be done, how it will be done, and what technology will be used to deliver the results.

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Apache Pulsar Architecture and Benefits

Introduction to Apache Pulsar

Apache Pulsar is a multi-tenant, high-performance server to server messaging system. Yahoo developed it. In late 2016 it was a first open-source project. Now it is in the incubation, under the Apache Software Foundation(ASF). Pulsar works on the pub-sub pattern, where there is a Producer, and a Consumer also called the subscribers, the topic is the core of the pub-sub model, where producer publish their messages on a given pulsar topic, and consumer subscribes to a problem to get news from that topic and send an acknowledgement.

Once a subscription has been acknowledged, all the messages will be retained by the pulsar. One Consumer acknowledged has been processed only after that message gets deleted.Apache Pulsar Topics:  are well defined named channels for transmitting messages from producers to consumers. Topics names are well-defined URL.

Namespaces:  It is logical nomenclature within a tenant. A tenant can create multiple namespaces via admin API. A namespace allows the application to create and manage a hierarchy of topics. The number of issues can be created under the namespace.

Apache Pulsar Subscription Modes

A subscription is a named rule for the configuration that determines the delivery of the messages to the consumer. There are three subscription modes in Apache Pulsar

Exclusive

In Exclusive mode, only a single consumer is allowed to attach to the subscription. If more then one consumer attempts to subscribe to a topic using the same subscription, then the consumer receives an error. Exclusive mode as default is subscription model.
 

Failover

In failover, multiple consumers attached to the same topic. These consumers are sorted in lexically with names, and the first consumer is the master consumer, who gets all the messages. When a master consumer gets disconnected, the next consumers will get the words.
 

Shared 

Shared and round-robin mode, in which a message is delivered only to that consumer in a round-robin manner. When that user is disconnected, then the messages sent and not acknowledged by that consumer will be re-scheduled to other consumers. Limitations of shared mode-

• Message ordering is not guaranteed.
• You can’t use cumulative acknowledgement with shared mode.

The process used for analyzing the huge amount of data at the moment it is used or produced. Click to explore about our, Real Time Data Streaming Tools

Routing Modes

The routing modes determine which partition to which topic a message will be subscribed. There are three types of routing methods. When using partitioned questions to publish, routing is necessary.

Round Robin Partition 

If no key is provided to the producer, it will publish messages across all the partitions available in a round-robin way to achieve maximum throughput. Round-robin is not done per individual message but set to the same boundary of batching delay, and this ensures effective batching. While if a key is specified on the message, the producer that is partitioned will hash the key and assign all the messages to the particular partition. This is the default mode.

Single Partition

If no key is provided, the producer randomly picks a single partition and publish all the messages in that particular partition. While if the key is specified for the message, the partitioned producer will hash the key and assign the letter to the barrier.

Custom Partition

The user can create a custom routing mode by using the java client and implementing the MessageRouter interface. Custom routing will be called for a particular partition for a specific message.

Apache Pulsar Architecture

Pulsar cluster consists of different parts in it: In pulsar, there may be one more broker’s handles, and load balances incoming messages from producers, it dispatches messages to consumers, communicates with the pulsar configuration store to handle various coordination tasks. It stores messages in BookKeeper instances.

• BookKeeper cluster consisting of one or more bookies to handles persistent storage of messages.
• ZooKeeper cluster calls the configuration store to handle coordination tasks that involve multiple groups.

Brokers

The broker is a stateless component that handles an HTTP server and the Dispatcher. An HTTP server exposes a Rest API for both administrative tasks and topic lookup for producers and consumers. A dispatcher is an async TCP server over a custom binary protocol used for all data transfers.

Clusters

A Pulsar instance usually consists of one or more Pulsar clusters. It consists of: One or more brokers, a zookeeper quorum used for cluster-level configuration and coordination and an ensemble of bookies used for persistent storage of messages.

Metadata store

Pulsar uses apache zookeeper to store the metadata storage, cluster config and coordination.

Persistent storage

Pulsar provides surety of message delivery. If a message reaches a Pulsar broker successfully, it will be delivered to the target that’s intended for it.

Pulsar Clients

Pulsar has client API’s with language Java, Go, Python and C++. The client API encapsulates and optimizes pulsar’s client-broker communication protocol. It also exposes a simple and intuitive API for use by the applications. The current official Pulsar client libraries support transparent reconnection, and connection failover to brokers, queuing of messages until acknowledged by the broker, and these also consists of heuristics such as connection retries with backoff.

Client setup phase

When an application wants to create a producer/consumer, the pulsar client library will initiate a setup phase that is composed of two setups:

1. The client will attempt to determine the owner of the topic by sending an HTTP lookup request to the broker. The application could reach to an active broker which in return by looking at the cached metadata of zookeeper will let the user know about the serving topic or assign it to the least loaded broker in case nobody is serving it.
2. Once the client library has the broker address, it will create a TCP connection (or reuse an existing connection from the pool) and authenticate it. Within this connection, binary commands are exchanged between the broker and the client from the custom protocol. At this point, the client sends a command to create consumer or producer to the broker, which complies after user validates the authorization policy.

Geo-Replication

Apache Pulsar’s Geo-replication enables messages to be produced in one geolocation and can be consumed in other geolocation.  In the above diagram, whenever producers P1, P2, and P3 publish a message to the given topic T1 on Cluster – A, B and C respectively, all those messages are instantly replicated across clusters. Once replicated, this allows consumers C1 & C2 to consume the messages from their respective groups. Without geo-replication, C1 and C2 consumers are not able to consume messages published by P3 producers.

Multi-Tenancy

Pulsar was created from the group up as a multi-tenant system. Apache supports multi-tenancy. It is spread across a cluster, and each can have their authentication and authorization scheme applied to them. They are also the administrative unit at which storage, message Ttl, and isolation policies can be managed.

Tenants

To each tenant in a particular pulsar instance you can assign:     

• An authorization scheme.     
• The set of the cluster to which the tenant’s configuration applies.

The Dataset is a data structure in Spark SQL which is strongly typed, Object-oriented and is a map to a relational schema.Click to explore about our, RDD in Apache Spark Advantages

Authentication and Authorization

Pulsar has support for the authentication mechanism which can be configured at the broker, and it also supports authorization to identify the client and its access rights on topics and tenants.

Tiered Storage

Pulsar’s architecture allows topic backlogs to grow very large. This makes a rich set of the situation over time. To alleviate this cost is to use Tiered Storage. The Tiered Storage move older messages in the backlog can be moved from BookKeeper to cheaper storage. Which means clients can access older backlogs.

Schema Registry

Type safety is paramount in communication between the producer and the consumer in it. For safety in messaging, pulsar adopted two basic approaches:

Client-side approach

In this approach message producers and consumers are responsible for not only serializing and deserializing messages (which consist of raw bytes) but also “knowing” which types are being transmitted via which topics. 

Server-side approach

In this approach which producers and consumers inform the system which data types can be transmitted via the topic. With this approach, the messaging system enforces type safety and ensures that both producers and consumers remain in sync.

How schemas work ?

Pulsar schema is applied and enforced at the topic level. Producers and consumers upload schemas to pulsar are asked. Pulsar schema consists of :

• Name: name is the topic to which the schema is applied.
• Payload: binary representation of the schema.
• User-defined properties as a string/string map

It supports the following schema formats:

• JSON
• Protobuf
• Avro
• string (used for UTF-8-encoded lines) 

If no schema is defined, producers and consumers handle raw bytes.

What are the Pros and Cons?

The pros and cons of Apache Pulsar are described below:

Pros

• Feature-rich – persistent/nonpersistent topics
• Multi-tenancy
• More flexible client API- including CompletableFutures,fluent interface
• Java clients have till date to no java docs.

Cons

•  Community base is small.
•  The reader can’t read the last message in the topic [need to skim through all the words]
•  Higher operational complexity – ZooKeeper + Broker nodes + BookKeeper + all clustered.
• Java client components are thread-safe – the consumer can acknowledge messages from different threads.

Apache Pulsar Multi-Layered Architecture

Difference between Apache Kafka and Apache Pulsar

Drawbacks of Kafka

1. High Latency
2. Poor Scalability
3. Difficulty supporting global architecture (fulfilled by pulsar with the help of geo-replication)
4. High OpEx (operation expenditure)

How Apache Pulsar is better than Kafka

1. Pulsar has shown notable improvements in bot latency and throughput when compared with Kafka. Pulsar is approximately 2.5 times faster and has 40% less lag than Kafka.
2. Kafka, in many scenarios, has shown that it doesn’t go well when there are thousands of topics and partitions even if the data is not massive. Fortunately, the pulsar is designed to serve hundreds of thousands of items in a cluster deployed.
3. Kafka stores data and logs in the dedicated files and directories (Broker) this creates trouble at the time of scaling (files are loaded to disk periodically). In contrast, scaling is effortless in the case of the pulsar as pulsar has stateless brokers that means scaling is not rocket science, pulsar uses bookies to store data. 
4. Kafka brokers are designed to work together in a single region in the network provided. So it is not an easy way to work with multi-datacentre architecture. Whereas, pulsar offers geo-replication in which user can easily replicate it’s data synchronously or asynchronously among any number of clusters.
5. Multi-tenancy is a feature that can be of great use as it provides different types of defined tenants that are specific to the needs of a particular client or organization. In layman language, it’s like describing a set of properties so that each specific property satisfies the need for a specific group of clients/consumers using it.

Even though it looks like Kafka lags behind pulsar, but kip (Kafka improvement proposals) has almost all of these drawbacks covered in its discussion and users can hope to see the changes in the upcoming versions of the Kafka.

Kafka To Pulsar –  User can easily migrate to Pulsar from Kafka as Pulsar natively supports to work directly with Kafka data through connectors provided or one can import Kafka application data to pulsar quite easily.

Pulsar SQL  uses Presto to query over the old messages that are kept in backlog (Apache BookKeeper).

Conclusion

Apache Pulsar is a powerful stream-processing platform that has been able to learn from the previously existing systems. It has a layered architecture which is complemented by the number of great out-of-the-box features like multi-tenancy, zero rebalancing downtime,geo-replication, proxy and durability and TLS-based authentication/authorization. Compared to other platforms, pulsar can give you the ultimate tools with more capabilities.

• Discover here about Stream Processing with Apache Flink
• Click to explore about Big Data Maturity Model

Original article source at: https://www.xenonstack.com/

#kafka #apache #architecture #benefits 

What Is Customer Identity and Access Management?

Customer Identity and Access Management provides the convenience of a centralized customer database that connects all other apps and services for a safe and seamless customer experience.
CIAM streamlines every business operation that involves dealing with individual consumers, including those who haven’t yet registered on your site.

Importance of CIAM

For Customers: Today, every business aspires to be a technology firm. Customer needs are shifting as a result of the expansion of channels, devices, platforms, and touchpoints. And having a secure experience with such interactions is crucial.

For Businesses: Traditionally, customer identity and access management has been a consumer use case (B2C). However, a firm might be a client of an organisation (B2B). As consumers demand more from the organisations with whom they do business, the new method of doing business encompasses a wide range of markets and use cases.

A CIAM solution includes various enterprise-level capabilities that can help increase security, improve customer data collection, and deliver crucial data to marketing and sales teams.

Benefits of Customer Identity and Access Management**

1. Data and Accounts Security
   A standard CIAM system includes security features that protect data as well as account access. Risk-based authentication, for example, monitors each customer’s usage and login trends, making it easier to notice anomalous (and thus potentially fraudulent) activities.

2. Each customer has a unified view
   You can acquire a complete picture of each consumer by linking the data from all of your services and websites. You can reach out to your consumers more easily and provide better service if you have a better understanding of them.

3. Advanced Login Options
   These login methods help customers have a better experience, get more trust, or do both.

(i) Passwordless Login makes the login process easier and more secure by eliminating the need for a password. It also assists you in presenting your business as a modern, secure corporation that employs cutting-edge technology to protect your clients.

(ii) Customers can also log in using a generated link sent to their email address or a one-time password texted to their phone using One-Touch Login. Unlike Passwordless Login, however, the consumer does not need to be a current user in the system, and no credentials are required.

(iii) Smart Login allows users to log in quickly and securely to the internet of things (IoT) and smart gadgets, which are becoming an increasingly important aspect of today’s digital ecosystem. Smart login delegated the authentication process for smart TVs, gaming consoles, and other IoT devices to other devices that make inputting and managing passwords easier and more safe.

Final Thoughts:
Many companies use a customer identity management system to provide their customers with a modern digital experience. Customer account information, including data, consent, and activity, can all be accessed from one dashboard with a CIAM system like LoginRadius.

#customer #identity #access #management #benefits #importance

A brief overview of PostgreSQL's advantages and drawbacks

A number of characteristics and features of Postgres make it appropriate for a very wide range of applications:

• Code quality
• Extensibility
• SQL and NoSQL
• Spatial data
• Data availability and resiliency

#postgresql #benefits #snapshot

Present-day Data Analytics: Applications and Benefits

How does the present-day data analytics look like

In today’s era, the amount of data available is on the increase with numerous organizations and businesses being able to accumulate data across their separate industries. Obviously, Data Analytics gives them a preferred position over their rivals to identify which fields in their products and services they need to enhance, where sales may have decreased or increased and where there may be a loophole in the market.

The term data analytics alludes to the way toward inspecting datasets to make inferences about the data they contain. Data analytic methods empower you to take raw data and reveal patterns to extract valuable insights from it

Today, numerous data analytics strategies utilize particular frameworks and programming that incorporate machine learning algorithms, automation and other technologies.

Applications of Data Analytics

Benefits of Data Analytics

#big data #latest news #data analytics #applications #benefits #present-day data analytics

The Benefits of Applying Analytics to Hidden Data Sources

Data usage to drive business insight has exploded. These days, every successful company is data-driven. The challenge that companies face these days is collecting more data than they bargained. As IoT and mobile devices continue to proliferate across industries, companies are increasingly sitting on data pools that remain untapped.

Bringing analytics to these hidden data pools will help organizations leap ahead of their competition. Here are 3 digital gold mines that are ripe for tapping.

Cost Center Processes

Employee Data

Customer Interactions

Untapped Gold Mines

#big data #latest news #benefits #applying analytics #hidden data sources #the benefits of applying analytics to hidden data sources

Benefits and Advantages of Big Data & Analytics in Business

By now, everyone has heard of Big Data and the wave it has created in the industry. After all, it’s always in the news – companies across various sectors of the industry are leveraging Big Data to promote data-driven decision making. Today, Big Data’s popularity has extended beyond the tech industry to include healthcare, education, governance, retail, manufacturing, BFSI, and supply chain management & logistics, to name a few. Almost every enterprise and organization, big or small, is already leveraging the benefits of Big Data.

According to Gartner, “Big Data are high volume, high velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”

In essence, Big Data refers to datasets that are too large or complex for traditional data processing applications (for instance, ETL systems). It is characterized by three core features – high volume, high velocity, and high variety. Rapid development and adoption of disruptive technologies (AI, ML, IoT), rapidly-growing mobile data traffic, cloud computing traffic, and high penetration of smartphones, all contribute to creating an ever-increasing volume and complexity of large datasets.

Since the advantages of Big Data are numerous, companies are readily adopting Big Data technologies to reap the benefits of Big Data. Statista maintains that the global big data market will grow to $103 billion by 2027, with the software industry leading the Big Data market with a 45% share. While the global Big Data and Business Analytics market was valued at $169 billion in 2018, it is estimated to rise to $274 billion by 2022. In 2018, nearly 45% of professionals in the market research industry used big data analytics as a research method.

You won’t belive how this Program Changed the Career of Students

Table of Contents

• Advantages of Big Data        
• Cost optimization
• Improve efficiency
• Foster competitive pricing
• Boost sales and retain customer loyalty
• Innovate 
• Focus on the local environment
• Control and monitor online reputation

#big data #benefits and advantages of big data & analytics in business #advantages #benefits #benefits and advantages of big data #analytics in business

What is a Chatbot and the Benefits of Using them?

Are you a new business looking for innovative ways to connect with your target audience? Have you recently seen other brands making use of chatbots and are wondering how you can make use of them for your own brand?

Businesses and brands are always looking for something new to improve their customer service, or their user experience. One of the many ways that businesses go about doing this is through chatbots. Although they aren’t necessarily new technology to the world, many people may not know too much about then, however, due to incredible technological advances, they have become quite sophisticated. One of the earliest examples that is quite commonly known to us is Siri.

If you don’t know what it is or what to know if it can help your business, here is everything you need to know about Chatbot’s

#chatbots #latest news #what is a chatbot and the benefits of using them? #benefits #benefits of chatbots

Top 7 Benefits of AWS - Advantages & Disadvantages of Amazon Web Services

Since its launch in the year 2006, AWS has become the undisputed cloud platform. It has been a successful Cloud Computing service in this competitive market due to the quality services and features it provides. But, have you ever wondered what exactly is AWS and why companies use it? Let’s go ahead and know what it is. In this blog, you will also come across the top AWS benefits as well as little known drawbacks.

What is AWS?

AWS is a Cloud Computing platform, which helps you build your applications over the cloud. It offers various services like a combination of infrastructure and software services, along with computing power, scalability, reliability, and secure database storage. You can use AWS for quality development as it offers around 200 products and services all over the world.

The top 5 services provided by Amazon Web Services are:

• Amazon Elastic Cloud Compute (EC2)
• Amazon Simple Storage Service (S3)
• Amazon Virtual Private Cloud (VPC)
• Amazon CloudFront
• Amazon Relational Database Services (RDS)

Now, let’s talk about the advantages and disadvantages of Amazon Web Services.

#aws #amazon web services #benefits

The Benefits of Cloud Computing

Have you ever considered converting your business’ IT system to a cloud infrastructure? Read on to discover all the benefits of doing so.

Cloud computing offers your business numerous advantages. It’s a virtual office that lets you set up to give you the flexibility to connect with your business anywhere, anytime. With the growing number of web-enabled devices used in today’s business environment (e.g., smartphones, tablets, etc.), accessing your data is much easier and safer.

Why Move to Cloud Computing?

There are so many benefits to moving your business to the cloud:

• Cost-effectiveness
• Reliability
• Scalability
• High Availability
• Security
• Performance
• Mobility
• Business Continuity
• Backup and Disaster Recovery
• Fast and Effective Virtualization
• Unlimited Storage Capacity
• Easy Management
• Quick Deployment

#cloud #benefits #cloud computing

Why is it Beneficial to Write Microservices with Golang?

Read this article and find out before you decide to hire Golang developers and work on your applications.

Many developers rely on the microservice structure because large, complex applications can work consistently through a mixture of remote assistance. Many well-known technical organizations, including multi-billion companies like Amazon, Walmart, and Netflix, have turned to microservices.

As companies strive to develop more extensive and complex administrations that can be partitioned and managed to create more minor co-operations, microservices are becoming more critical. More and more people are trying to transform their conventional unified regularity into a series of unique, self-governing microservices.

What is a Microservice?

Microservices is a kind of Service-Oriented framework technique. It constructs the apps as a collection of vaguely linked co-operations. In this structure, the encryption service usually is subtle, and the rules are not so demanding. Suppose we are looking for a precise explanation of microservices. There is no definition in that case, but some of its features are related to automatic configuration, entrepreneurship, decentralized data control, and endpoint knowledge.

Advantages that microservice architecture will provide:

This structure facilitates us to image the complete software in components or miniature modules, making it less complicated to learn, expand, and examine.

It facilitates us to understand the offerings as assigned but indeed defines their value withinside the software. On the pinnacle of that, it facilitates the extra mission resistance to structural corrosion.

#microservices #backend #golang #benefits