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Big data is one of the hottest buzzwords and trends in technology for quite some time now. However, beyond the buzz of the media, Big data is a fundamental technology in building future businesses and sustainable enterprises. It is what allows businesses to better understand their clients and make better decisions based on that understanding, it also allows decision makers to predict and understand failures, errors, threats as well as opportunities and hence create more sustainable systems in return. It’s true that data has existed for as long as we could remember, but the exponential increase in volume, variety, and velocity of that data is what earned it the name of Big Data. We generate quintillion bytes of data every day, more than 500 thousand photos are being shared on Snapchat, 4 million videos being viewed by users and 3+ million search queries being conducted on Google in a single minute. The sheer volume of valuable insights in that enormous amount of data creates the need for Big Data frameworks, to manage and analyze the data with the resources at hand.Hadoop
Hadoop is an Apache open source framework for managing and processing datasets. Hadoop uses computer clusters and modules that are designed to be fault-resistant. Based on the belief that hardware will fail, at one point or another, it replicates the data and copies it to another node in the cluster so that if a failure took place, the data could be retrieved through the other node and saving the hustle of data inconsistency. That is not the only benefit that Hadoop offers, though, Apache Hadoop is scalable which is an important requirement in the Big Data world, as users should have the ability to expand their infrastructure and distribute a large amount of data over multiple servers that could be potentially working in parallel. Not only that, but it is also a fast and flexible framework, the way Hadoop maps the data on the clusters empowers data processing and make it faster. Hadoop comes with four modules: Hadoop Common, Hadoop Distributed File system, Hadoop YARN (Yet Another Resource Negotiator) and Hadoop MapReduce. While Hadoop is a widely known and loved framework, there are few cons to using Hadoop such as lack of preventive measures as the default of security measures in Hadoop is being disabled, so scientists and users should always keep that in mind while working with sensitive data. Also, it’s hard to use for small data, so it’s almost only fit for usage at large companies or entities that generate or possess a large amount of data.Apache Spark
Spark is another amazing product made by Apache for batch processing. It’s a fairly easy to use framework, that enables users to write applications in Java, R, Python, Node Js and Scala. Although, an ideal situation occurs if the developer knows or tends toApache Storm
Storm is another framework offered by Apache for data processing, specifically, real-time processing. It is simple and can be used with any programming language, which allows you to use it with your favorite language and it is said to be fun to use as well. The core idea of Storm is defining certain small and discrete operations which creates a topology that functions as a pipeline for transforming the data. Storm is used for real-time analytics, continuous computations, online ML and much more. According to Apache,” a benchmark clocked spark at over million tuples processed per second per node”. Spark is also scalable and fault-tolerant.Apache Samza
Apache Samza is a powerful framework for asynchronous stream processing in real-time, which utilizes Apache Kafka for messaging and Hadoop YARN for fault tolerance, security and resource management. Samza offers a suite of great features such as a simple API that is comparable to MapReduce, processor isolation, durability, scalability and the fact that is Pluggable and lets you run Samza with other execution environments. The framework is made to handle many gigabytes per partition i.e. large amounts of states. It snapshots and restores processor’s state and is capable of restoring to a consistent snapshot upon restarting. Samza is best used for filtering, joins, distributed RPC and reprocessing.Wrap-up
There is also Apache Flink which we can’t go by without mentioning, it’s a powerful framework for both real-time and batch modes. It offers many high-level functionalities while being similar to MapReduce and is absolutely stunning when it comes to performance. The key point in comparing the frameworks and the main criterion is the environment and application that they will be used for, as the nature of the data and the environment shifts the need from one framework to another. Also, the use of a wrong framework for a situation would result in wasted resources.
Big data is one of the hottest buzzwords and trends in technology for quite some time now. However, beyond the buzz of the media, Big data is a fundamental technology in building future businesses and sustainable enterprises. It is what allows businesses to better understand their clients and make better decisions based on that understanding, it also allows decision makers to predict and understand failures, errors, threats as well as opportunities and hence create more sustainable systems in return. It’s true that data has existed for as long as we could remember, but the exponential increase in volume, variety, and velocity of that data is what earned it the name of Big Data. We generate quintillion bytes of data every day, more than 500 thousand photos are being shared on Snapchat, 4 million videos being viewed by users and 3+ million search queries being conducted on Google in a single minute. The sheer volume of valuable insights in that enormous amount of data creates the need for Big Data frameworks, to manage and analyze the data with the resources at hand.Hadoop is an Apache open source framework for managing and processing datasets. Hadoop uses computer clusters and modules that are designed to be fault-resistant. Based on the belief that hardware will fail, at one point or another, it replicates the data and copies it to another node in the cluster so that if a failure took place, the data could be retrieved through the other node and saving the hustle of data inconsistency. That is not the only benefit that Hadoop offers, though, Apache Hadoop is scalable which is an important requirement in the Big Data world, as users should have the ability to expand their infrastructure and distribute a large amount of data over multiple servers that could be potentially working in parallel. Not only that, but it is also a fast and flexible framework, the way Hadoop maps the data on the clusters empowers data processing and make it faster. Hadoop comes with four modules: Hadoop Common, Hadoop Distributed File system, Hadoop YARN (Yet Another Resource Negotiator) and Hadoop MapReduce. While Hadoop is a widely known and loved framework, there are few cons to using Hadoop such as lack of preventive measures as the default of security measures in Hadoop is being disabled, so scientists and users should always keep that in mind while working with sensitive data. Also, it’s hard to use for small data, so it’s almost only fit for usage at large companies or entities that generate or possess a large amount of data.Spark is another amazing product made by Apache for batch processing. It’s a fairly easy to use framework, that enables users to write applications in Java, R, Python, Node Js and Scala. Although, an ideal situation occurs if the developer knows or tends to learn Node Js for a better implementation of Apache Spark. Thanks to its DAG scheduler, physical execution engine, and query optimizer, it is hundred times faster than Hadoop is. Spark is perfect when it comes to machine learning too, but it then requires a cluster manager and a distributed storage system. Spark can be used as a standalone framework or can be used in conjunction with other frameworks as it supports the integration with Hadoop. Apache Mesos, Cassandra, HBase and kurbenetes which makes it ideal for most types of integrations, whether on an individual small scale or a large scale for businesses. Spark is based on a type of data structures known as RDD or Resilient Distributed Dataset. It is a read-only set of items that are distributed over the whole cluster of machines in the system. RDD makes the system fault-resistant as well and prevents the loss of data in cases of failures. Spark comes with a stack of libraries that empowers its functionality even more, that includes Spark SQL, Spark Streaming, MLib, Graphx. The spark code is reusable as well which makes it even friendlier and easier to use. However, there are cons to using Spark too, as it’s not too small-data friendly when it’s used with Hadoop either, and the RDD structure poses a high memory cost in return for the performance.Storm is another framework offered by Apache for data processing, specifically, real-time processing. It is simple and can be used with any programming language, which allows you to use it with your favorite language and it is said to be fun to use as well. The core idea of Storm is defining certain small and discrete operations which creates a topology that functions as a pipeline for transforming the data. Storm is used for real-time analytics, continuous computations, online ML and much more. According to Apache,” a benchmark clocked spark at over million tuples processed per second per node”. Spark is also scalable and fault-tolerant.Apache Samza is a powerful framework for asynchronous stream processing in real-time, which utilizes Apache Kafka for messaging and Hadoop YARN for fault tolerance, security and resource management. Samza offers a suite of great features such as a simple API that is comparable to MapReduce, processor isolation, durability, scalability and the fact that is Pluggable and lets you run Samza with other execution environments. The framework is made to handle many gigabytes per partition i.e. large amounts of states. It snapshots and restores processor’s state and is capable of restoring to a consistent snapshot upon restarting. Samza is best used for filtering, joins, distributed RPC and reprocessing.There is also Apache Flink which we can’t go by without mentioning, it’s a powerful framework for both real-time and batch modes. It offers many high-level functionalities while being similar to MapReduce and is absolutely stunning when it comes to performance. The key point in comparing the frameworks and the main criterion is the environment and application that they will be used for, as the nature of the data and the environment shifts the need from one framework to another. Also, the use of a wrong framework for a situation would result in wasted resources. A research by the Avendus Capital in 2023 indicated that the big data market in India was hovering around US$1.15 billion. It is estimated that India alone will face the shortage of 250,000 data scientist and engineer by the end of this year, which marks the insanely massive potential waiting in the big data field.
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Difficulties with page rendering and DOM manipulation
To avoid such issues what you can do is:
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Design core functionalities based on library
Try to create small pieces of functionalities with lesser dependencies
4. Karma: Karma is an open source productive testing environment. Easy workflow control Running on the command line. Offers the freedom to write the tests with Jasmine, Mocha, and QUnit. You can run the test on real devices with easy debugging.
More than these with upcoming challenges in performing testing there, some more powerful frameworks and tools may get evolved in future.
According to big data statistics, 90% of business leaders consider data and analytics as key to their organization’s digital transformation. Yet, they can only use 12% of their data. Such stark differences between goals and current capabilities exist in numerous tech domains and batch processing can help close these gaps.
Automated batch processing is a method for organizations to collect, store and process large amounts of data and transactions in batches simultaneously and continuously with no or little human intervention (See Figure 1).
Business executives must be aware of batch processing applications to identify similar use cases in their organizations. Therefore, we gathered the most common 41 batch processing applications and assigned them to 11 categories (e.g. general processes, sales, finance, customer service management, and industry-specific industries).
Figure 1: The way batch processing works in comparison to real-time analysis.General batch processing applications
1. Data Processing: Batch processing can process large volumes of data in batches. Some examples of data processing are data cleansing, aggregation, and transformation.
2. Report Generation: Batch processing can analyze and summarize data to create reports for financial, operational or performance reporting.
3. Backup and Recovery: Batch processing can ensure data integrity and availability by applying backup scheduling, backup file management, and data restoration.
4. Batch Job Scheduling: Batch job scheduling is another type of batch processing application that can schedule and run a series of batch jobs (e.eg. monitoring and execution) automatically.
5. Integration and Interoperability: Batch processing can help with integration and interoperability between different systems and applications through data exchange, synchronization, and integration.Applications in Business Functions Sales
7. Sales Reporting: Sales reporting involves analyzing sales data to gain insights into the sales process. Batch processing can automate sales reporting, allowing sales teams to quickly and accurately analyze big data.
8. Order Processing: Sales teams can use batch processing to manage the order fulfillment process, track inventory levels, and manage customer information faster and more accurately.
10. Customer intelligence: Batch processing can investigate sales data to identify customer behavior, buying patterns, and trends.Marketing
11. Email Marketing: Batch processing can seamlessly send large volumes of emails to a list of subscribers in batches, ensuring quick and efficient delivery of emails.
13. Social Media Marketing: Batch processing can be useful for social media marketing since it can schedule and publish posts on social media platforms. This way, marketers can save time and ensure that posts are published consistently.Finance
15. End-of-day processing: Financial services companies can leverage batch processing to automatically and accurately perform end-of-day processing to reconcile transactions, generate reports, and other tasks.
16. Fraud detection: Batch processing can detect fraudulent transactions by analyzing large volumes of data and identifying patterns and anomalies.
17. Risk management: Batch processing can help the finance sector identify and mitigate risk in a more data-driven way by analyzing data and identifying potential risks.
18. Compliance: Batch automation can simplify complying with regulatory requirements for financial services companies by automating compliance tasks and ensuring the consistency and accuracy of the data.IT processes
19. System Monitoring: Batch processing can allow teams to process system logs, event data, and metrics to detect and resolve issues quickly.
20. Reducing manual activities: With batch processing, IT teams can schedule and automate recurring tasks, such as database backups, patching, and system maintenance. This way, IT organizations can save time for more value-added tasks and ensure that critical tasks are completed on time.
21. Resource management: Batch processing allows IT teams to manage server resources, allocate memory and disk space, or optimize database performance.
22. Optimize costs: With batch processing, IT organizations can reduce the costs allocated for expensive hardware or infrastructure, saving on operational costs by running jobs during off-peak hours.Customer service
23. Ticket Management: Another batch processing application is the management of high volumes of service requests, incidents, and changes. Therefore, IT teams can prioritize and handle tickets more efficiently.
24. Data management: Batch processing can ensure data integrity and efficiency by backing, archiving and transforming the company data.
25. Feedback Management: With batch processing, customer service teams can process and analyze customer feedback in batches, such as surveys and reviews, to identify trends and issues more efficiently.Logistics
26. Order Processing: Batch processing can help logistics teams handle orders more efficiently by autımatically fulfilling a large volume of orders.
27. Inventory Management: Batch processing can manage inventory by automatically tracking stock levels, shipments, and other information.
28. Shipping and Tracking: Batch processing can ease the processing and tracking of a large volume of shipments and delivery information, improving the accuracy of the information shared with customers.
29. Improved performance: With batch processing, logistics teams can improve supply chain performance, demand forecasting, and operational efficiency.Retail
30. Promotions and Discounts: Batch processing can allow retailers to apply discounts to a large volume of products by processing data in batches.Telecom
31. Billing and Payment Processing: Batch processing can ensure telecom companies process and manage billing and payment more efficiently.
32. Managing Call Detail Records (CDR): With batch processing, telecom companies can process and manage a large volume of call data, such as call duration, location, and usage.
33. Network Traffic Analysis: Batch processing can analyze large volumes of network traffic data to gain insights into network performance, traffic patterns, and usage.
34. Detecting fraud: Batch processing can help in fraud detection, such as call spoofing or SIM cloning.Healthcare
35. Electronic Health Records (EHR) management: Batch processing can help process and manage electronic health records (EHR), including patient records, lab results, and clinical notes.
36. Improving Medical billing: Batch processing helps improve billing accuracy by automatically processing and submitting medical bills to insurance companies or patients.
37. Easy patient care & diagnosis: Batch processing can optimize patient care and diagnosis by analyzing large volumes of patient data, such as medical history, demographics, and clinical outcomes.Education
38. Student Records: Automated batch processing can be useful to manage student records, such as admissions, enrollment, grades, and transcripts, more efficiently.
39. Course Management: Batch processing can allow educational institutions to manage courses and course-related data, such as course schedules, assignments, and exams.
40. Financial Aid Processing: Another batch processing application is streamlining financial aid management where educational institutions can manage financial aid, such as scholarships, grants, and loans, more efficiently and with fewer errors.
41. Using educational data: Educational institutions can leverage batch processing to analyze data and gain insights into student performance, attendance, and behavior.Further reading
Explore more on batch automation and workload automation by checking out:
If you believe your organization can benefit from automated batch processing or other workload automation tools, use our data-driven and comprehensive list of WLA vendors.
Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.
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4 best tools for multi-store eCommerce with one database
It can be difficult to manage multiple stores but that’s why there are multi-store eCommerce tools.
Multiple eCommerce stores with one database can be managed from the same dashboard.
The first solution from our list is a customer helpdesk tool that will boost your sales quickly.
Check all our suggestions because you will also find a lot of flexible and customizable solutions.
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Managing one store can be challenging if you have a lot of products but if you have multiple stores that rely on the same database things get complicated.
You can have a whole team to manage the supply and demand for each store or you can use multi-store eCommerce software even if you have only one database.
Whether we’re talking about product management or customer service, you need an aggregated solution that will help you get the best of your resources.
There are plenty of such solutions on the market but only a few or just one will meet all your requirements so make sure you check the best multi-store eCommerce platforms.
Znode is a multi-store eCommerce solution that allows easy management of your stores, your sites, and brands from one single place.
At the base of this solution, there is a PIM (Product Information Management) tool with a flexible data structure that allows you to share the same catalog for many stores, simplifying the catalog management.
Actually, you can do that with themes, categories, payments, accounts and users, taxes. You can apply them at one or more stores, or individually to each of them.
The way you pay for Znode is also flexible and it depends on the complexity of the services you choose for your stores. It can be applied by profile, customer, account, by store, exactly the way that makes it more convenient.
Let’s see a couple of Znode’s top features:
Flexible structure and tools that can be personalized to your demands
Multi-brand and multi-site capabilities
Tools for creating regional eCommerce stores
B2B presence with account-based pricing, and a B2B2C presence
The ability to test and launch new business models
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If you become a tycoon and your sales are only going up, you need some help in creating a store network with personalized features to grow your brands even more.
That’s what StoreHippo is for, creating multiple, unique storefronts but with centralized inventory and customer data, all managed from the same dashboard.
You can create geo-located based stores in multiple cities or even countries, you can create affiliate stores with unique URLs and multiple keywords for each store, and many more.
You can even set overrides for the prices of each product in each store based on location or other conditions and create store-specific discounts. StoreHippo can be the way to start off your chain so try it.
Multiple store management from a single dashboard
Create unique storefronts for each store
Create different stores for each geographic location
Create price overrides and discounts for any product on each store
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AmeriCommerce is called by its creators as the industry standard for multi-store management. And that’s because you will be able to manage your customers, inventory, and content from a single console.
You will benefit from a lot of B2B features and customization tools and be able to use the same database for all your stores, thus simplifying the product management.
Of course, you can have different prices for each store, different shipping settings, you can apply discounts but you can also display the same products in all the stores.
You can have custom domain URLs, design themes for each store, and edit every single aspect of the design. AmeriCommerce is great for agencies and resellers so give it a try.
⇒ Get AmeriCommerce
Wix eCommerce is the go-to platform when it comes to professional online store websites that bring conversion rates.
You can efficiently build your online store using this website solution that includes 500+ free templates ready to generate profit.
It’s possible to go from scratch and design your online store in only a few steps. You just have to log in to your account and start customizing your multi-store website.
There are practical tools you can utilize to establish your brand for your online store. At the beginning, you have tools like logo maker or business name generator.
After the first steps, you can start to develop your digital store and manage your inventory, add thousands of products and collaborate with other suppliers.
Let’s see what are the key features of Wix eCommerce:
Customizable cart and checkout
Automated sales tax
Limitless customization with APIs
Integrated shipping solutions
Advanced payment tools
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4 Best Antivirus For Streamers [Top Picks] Pick an antivirus with gaming modes for the best performance
If you’re an online streamer, your security is paramount, and today we’ll going to show you the best antivirus for streamers.
All the applications mentioned in this guide offer amazing protection against all types of malware.
In case you need the best lightweight antivirus, feel free to consider any software from this guide.
If you’re streaming online, you need a secure antivirus that won’t interfere with your streaming. To ensure that, you need an antivirus with the Gaming Mode feature.
In addition to the Gaming Mode, you need a reliable antivirus that can protect you from all types of malware and phishing attacks. And to get the maximum performance, it’s recommended to use an antivirus with low resource usage.
In this guide, we’re going to present to you the best antivirus for streams that will let you stream online without any slowdowns.What is the best antivirus to use while streaming?
If you’re a gamer and a streamer, you should consider using ESET Internet Security. The software is available on multiple desktop platforms, and it will protect you from all types of online threats.
The software also offers privacy protection, so you’ll be completely safe while making online purchases. This antivirus can also check your router and smart devices for vulnerabilities, and it can even block access to your webcam.
ESET Internet Security is lightweight, and it uses minimum power, so you won’t experience any slowdowns while streaming resource-intensive games.
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Regarding your privacy, the software has anti-phishing, anti-fraud, antispam, and web attack prevention features, so your personal information will remain safe.
Bitdefender has a Gaming Mode feature, as well as webcam protection, and since it’s lightweight, you can stream and play games without any slowdowns.
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If you’re looking for the best antivirus for streamers, you might want to consider using Avira. This software will block malicious websites, ransomware, and other online threats.
The application has its own firewall that will block access to your device and keep you safe. Regarding privacy, the software has built-in anti-scam protection that will warn you about phishing websites.
As for additional features, the software offers a free VPN, as well as free tune-up tools, a software updater, and a password manager, which makes it a perfect all-around solution.
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Another great antivirus for streamers is Avast Free Antivirus. The software is available on multiple platforms, and thanks to machine-learning virus protection, it will protect you against all types of malware.
The software has a Behavior Shield feature, so it will analyze software behavior and block any suspicious activity. Of course, a gaming mode feature is available, so you can enjoy multimedia without distractions.
Using Avast Free Antivirus you can also scan your system for vulnerabilities, such as weak passwords, out-of-date software, etc. The antivirus is lightweight, so it can protect your PC without any slowdowns, so it’s perfect for streaming.
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Picking the best antivirus for streamers isn’t an easy task, but we hope that this guide helped you find the perfect software for your needs.
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Big Data is on the rise, with analysts and pundits almost unanimously predicting rapid adoption and growth. IDC, for one, predicts that the market for Big Data products will reach $16.1 billion by the end of this year and hit $41.5 billion by 2023, growing six times faster than the overall IT market.
Investors are also pouring money into Big Data startups, with the biggest splash being Cloudera’s billion-dollar investment from its partnership with Intel.
As more and more companies jump on the Big Data bandwagon – a recent Gartner survey found that 73 percent of all businesses are already investing in or have plans to invest in Big Data – IT will begin to get pressure to help turn these investments into actual business initiatives.
Here are 4 Big Data strategies IT should steal from marketers:
We all hate high-pressure, high-B.S. sales tactics. We avoid the Glengarry, Glen Ross sales types, who think of selling as a competition – with you as the mark. In the age of social media and Big Data analytics, it’s pretty easy to prove that these techniques aren’t optimal.
Yet, many, many businesses still employ them. Many businesses still treat their prospects as flesh-and-blood piggybanks that they’re eager to crack open.
Similarly, IT all too often treats the people seeking its help as nuisances, rather than as business assets.
It’s become a cliché that the most important asset in any business is its people, yet the biggest complaint about IT support interactions tends to be that IT comes off as too arrogant and is too dismissive of the people it serves.
How do you counter this?
A good place to start is with all of those calls that are supposedly recorded for training purposes. Rather than simply storing them for compliance purposes and then forgetting about them, now is the time to actually start analyzing them.
Lesson for IT: Tools from companies like CallMiner, Nexidia, and Utopy will help your organization apply text and sentiment analysis to calls to help you identify patterns and trends. Over time, effective techniques will stand out, as will effective members of your IT support staff.
Developing a proprietary audience involves nurturing and engaging with a person from the minute they become aware of you on through to when they follow you, sign up for your email newsletter, share your messages, and, eventually, become a loyal repeat customer. The key, though, is to tailor your communications, so they match where people are actually at along that path.
“If you send people the wrong message at the wrong time, you can do more harm than good,” Rohrs told me when we sat down at the Connections conference earlier this fall to discuss his book.
Marketers have woken up to the fact that they need to focus, in granular detail, on the needs of their audience, needs that evolve over time. Yet, the only way to better target your audience is to figure out who, exactly, these people are.
“Most companies have not taken the time to differentiate their audiences,” Rohrs said. “Contacts are strewn across different channels, databases, and teams, and there is no real strategy for engaging them.” As a result, audiences are regarded more like resources to use up, rather than business assets to cultivate and serve.
Rohrs identifies three main audience segments that marketers should focus on: seekers, amplifiers, and joiners.
Seekers are looking for information or distractions. This is what pretty much all of us do when we browse social media looking for interesting articles. Amplifiers are on the hunt for things they want to share with their own followers. Amplifiers have their own large audiences, and they are the fuel that powers any viral campaign. Then, finally, there are joiners, or the people who actually purchase your products or services.
Of course, people inhabit different roles at different times, so those roles can and should evolve, but it’s important to tailor your message to where people are now, not where you’d like them to be.
Lesson for IT: Start studying every interaction you have with the people you serve to see if you can segment them into more discrete audiences. After all, the person you need to remind to check to be sure their power strip is turned on will have much different needs than a tech-savvy person frustrated by some software glitch. Yet, all too often, each person is queued up the same way, which is wildly inefficient.
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