November 2019 – AnalyticsCube

Nov 28, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

SQL Database Source

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:What is an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, cant use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cooks distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesnt perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobis distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

Using Analytics to build A #BigData #Workforce

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[ QUOTE OF THE WEEK]

Data that is loved tends to survive. Kurt Bollacker, Data Scientist, Freebase/Infochimps

[ PODCAST OF THE WEEK]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

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[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

OEM Architecture: What’s Right For Your Business?

Thereâs no denying it: the OEM analytics market is huge. A trend now recognized by Gartner in their 2018 Critical Capabilities for Analytics and Business Intelligence Platforms report, more and more organizations see the value in providing their customers with analytics.

But whatâs the best way to architect your OEM offering? This is where the fun begins.

But First! White Labeling vs. Embedding

Here at Sisense, there are two relevant use cases for OEM platforms: white labeling or embedding. Often mistaken for one another, there are some differences between the two.

In the white-labeled use-case, a Sisense server is completely rebranded so that the Sisense logo and name is replaced, the color palette is changed to the match the organizationâs application, system emails are customized by the system to match the brand and more. The tenants (our customerâs customers) access the Sisense server directly, and experience Sisense analytics as provided by the customer. They are not necessarily aware that the analytics server they are accessing is actually a Sisense server.

In the embedded use-case, our customers have their own web application and embed Sisense within it. We support embedding the full Sisense application, including the data modeling, analytics and administration areas, or embedding specific dashboards and widgets using IFrames. Widgets can also be embedded within web pages using the SisenseJS infrastructure.

Three Flexible OEM Architectures

Option 1: Shared ElastiCubes with row-based data security

The first type of architecture for OEM deployments utilizes shared Sisense servers for multiple tenants and shared ElastiCubes and dashboards. Segregation between tenants is achieved by using row-based data security within ElastiCubes. All of the customersâ data resides in a shared ElastiCube, but each of the tenants gets access only to their own data.

Advantages: Lower hardware costs, high resource utilization, simple asset change management
Disadvantages: Tenant resource usage may affect other tenants
Typically Best for: Tenants with identical data models and dashboard requirements

Option 2: Dedicated ElastiCube per tenant

The second architecture for OEM deployments utilizes shared Sisense servers for multiple tenants, together with providing a dedicated cube and dashboards for each tenant. In this deployment, multiple tenants use the same server.

Looking for an even deeper understanding of OEM architectures? Read an in-depth look at each of the three options described above in our whitepaper.

Typically, the OEM has default ElastiCubes and dashboards and creates a dedicated copy of them for each of the tenants. The ElastiCubes and dashboards can be identical copies for each of the tenants or customized per tenant. The OEM uses access control for ElastiCubes and dashboards to ensure each of the tenants only has access to their own data. Typically a user group is created for each of the tenants. All of the tenantâs users are assigned to the same group. The relevant ElastiCubes and Dashboards are shared with the tenantâs group. In this way, the asset access control layer ensures that users of each tenant only gets access to their own data.

With this solution, you need to consider how the system scales to support your future needs and support additional tenants. While initially, youâll enjoy shared server resources, as you add more tenants you may have to provision additional servers, increasing the hardware costs of this solution.

Advantages: Low hardware costs for a small number of tenants, high resource utilization
Disadvantages: High hardware costs for a large number of tenants, more complicated asset change management, tenant resource usage may affect other tenants
Typically Best for: Tenants who require customized data models

Option 3: Dedicated Sisense server per tenant

The third architecture for OEM deployment is to provide a dedicated Sisense server for each tenant. Typically an OEM customer will have a server image including default ElastiCubes and dashboards. Each of the customers receives their own instance of the server. The ElastiCubes and dashboards can be identical between the servers or customized for each of the tenants. The data for each tenant is completely separate as each server has its own assets, including configuration, users, ElastiCubes, and dashboards.

Advantages: Highest level of security, dedicated resources per tenant
Disadvantages: Low resource utilization, higher hardware costs, complicated asset change management
Typically Best for: Tenants with strict security regulations, such as financial or healthcare institutes and tenants that need a high level of schema and dashboard customizations

Whatâs The Right Choice?

The type of architecture suitable for a specific customer depends on the use-case, the needs of the customer, the resources that can be dedicated to the deployment (both allocated people, and allocated hardware), and preferences. Of course, in order to make the right decision for you and your business, itâs always best to review all options in relation to your current and ongoing needs.

Looking for an even deeper understanding of OEM architectures? Read an in-depth look at each of the three options described above in our whitepaper.

Source: OEM Architecture: Whatâs Right For Your Business? by analyticsweek

Nov 21, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Convincing Source

[ AnalyticsWeek BYTES]

>> Using Task Ease (SEQ) to Predict Completion Rates and Times by analyticsweek

>> Dell to create Big Data skills in Brazil by analyticsweekpick

>> Dec 27, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

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[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. Alvin Tof

[ PODCAST OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.

Sourced from: Analytics.CLUB #WEB Newsletter

Which New Features Do Users Want? Decoding Customer Requests

Like every other feature in your application, the world of embedded analytics is not static. The outer bounds of capabilities customers want are constantly evolving. At the same time, older capabilities like data visualizationsâwhich customers once considered modern and innovativeâare now table stakes.

Your end users will always have new requests (and complaints) about your applicationâs embedded analytics. Itâs inevitable. And as long as everythingâs working as it shouldâ youâre keeping bugs in check, your app is reliableâmost complaints are likely new feature requests in disguise.

Unfortunately, translating those requests into actual analytics features can be difficult. What do your users really want from their dashboards and reports? Decoding these complaints means adding valuable new features to your roadmap, and avoiding a panicked scramble to add them before itâs too late and your customers start churning.

Use this chart to translate common end user requests (on the left) into the analytics features your users really want (on the right):

If Youâre Hearing Thisâ¦	Â â¦Then Consider Adding This Analytics Capability to Your Application
âWe need insights on whatâs likely to happen in the future so we can figure out how to correct issues before they become disastrous.â	Predictive Analytics
âThe data is great, but itâs in a vacuum and not changing the way we do business.â	Workflow Capabilities
âWhen weâre using the analytics, it feels like we have to learn an entirely new application.â	White Labeling
âUsers dislike having to log in twice (once to the app, once to the dashboards). Plus, the application admins say itâs a pain to manage security settings in two different places.â	Integrated Security/ Single Sign-On
âWhen we need to update the information in the dashboard, we donât like having to leave the app to do so.â	Write-back
âWe have to create multiple new reports just to view different cross sections of data, such as different product lines or date ranges. Itâs tedious and inefficient.â	Personalization/Security
âOur users need to access info from the field and the dashboards donât work well on mobile devices.â	Mobile Responsive

To learn more, get ourÂ Blueprint for Modern Analytics >

Source: Which New Features Do Users Want? Decoding Customer Requests

Nov 14, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data Accuracy Source

[ AnalyticsWeek BYTES]

>> Solving Common Data Challenges by analyticsweek

>> Sisense AI â What it Really Takes to Build a Better Mousetrap by analyticsweek

>> Three Upcoming Talks on Big Data and Customer Experience Management by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:How frequently an algorithm must be updated?
A: You want to update an algorithm when:
– You want the model to evolve as data streams through infrastructure
– The underlying data source is changing
– Example: a retail store model that remains accurate as the business grows
– Dealing with non-stationarity

Some options:
– Incremental algorithms: the model is updated every time it sees a new training example
Note: simple, you always have an up-to-date model but you cant incorporate data to different degrees.
Sometimes mandatory: when data must be discarded once seen (privacy)
– Periodic re-training in batch mode: simply buffer the relevant data and update the model every-so-often
Note: more decisions and more complex implementations

How frequently?
– Is the sacrifice worth it?
– Data horizon: how quickly do you need the most recent training example to be part of your model?
– Data obsolescence: how long does it take before data is irrelevant to the model? Are some older instances
more relevant than the newer ones?
Economics: generally, newer instances are more relevant than older ones. However, data from the same month, quarter or year of the last year can be more relevant than the same periods of the current year. In a recession period: data from previous recessions can be more relevant than newer data from different economic cycles.

Source

[ VIDEO OF THE WEEK]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. Jeff Weiner

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

iTunes GooglePlay

[ FACT OF THE WEEK]

According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 07, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Fake data Source

[ AnalyticsWeek BYTES]

>> Does the Future Lie with Embedded BI? by analyticsweek

>> How can you reap the advantages of Big Data in your enterprise? Services you can expect from a Remote DBA Expert by thomassujain

>> Voices in AI â Episode 91: A Conversation with Mazin Gilbert by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What is POC (proof of concept)?
A: * A realization of a certain method to demonstrate its feasibility
* In engineering: a rough prototype of a new idea is often constructed as a proof of concept

Source

[ VIDEO OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. Hal Varian

[ PODCAST OF THE WEEK]

@ReshanRichards on creating a learning startup for preparing for #FutureOfWork #JobsOfFuture #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

And one of my favourite facts: At the moment less than 0.5% of all data is ever analysed and used, just imagine the potential here.

Sourced from: Analytics.CLUB #WEB Newsletter

A Single Customer View : The Secret Weapon Everyone Must Use

Insurers have a lot of customer data across different systems peppered throughout their enterprise. Customer data would typically live in multiple systems like CRM, Billing, Policy Administration, and so on. This approach however suffers from multiple challenges:

Duplicate data across multiple systems
Multiple Versions of the same data point
No Single source of truth
No correlation between cause and action
Completely under utilized customer interaction data

Imagine a structure so flexible and scalable that, that it could bring all your data sources together, irrespective of the data formats, tie in with the customer key result areas (KRAs) and at the same time deliver predictable insights at the point of decision. In real time.

Enter single customer view. Or as we call it â Customer OneView.

OneView in Action

Customer OneView is part of Aureus data analytics platform called CRUX. OneView is not anything like a CRM. While a CRM would show only static information, OneView delivers intelligent, usable and real time insights that can be put to use immediately.

OneView can integrate with (atleast) four broad event data streams:

Customer
Relationship
Transactions
Interaction

Nitin had written about stream based data integration on his insightful post titled âCheers to Stream Based Integrationâ

These data streams could originate across multiple data systems â Policy Admin, CRM, Billing, etc.. Between them, these four cover some of the most critical customer data, that often lies under utilized. OneView not only brings these data streams together, but it also helps build a comprehensive customer life journey showing important milestones, critical customer interactions, sentiment at each interaction or transaction level as well as at a relationship level.Â While OneView is a powerful insights delivery framework, it also helps to deliver the output of predictive analytics models in a form that is usable by the business users.Â OneView can help translate the output of the analytical models into usable insights. Imagine a customer sales representative talking to a customer, or a field sales agent going to meet a customer. OneView will give them unambiguous insight into the customers history, sentiment and even potentialÂ action to take, without burdening them with the Hows and Whys.

Imagine a typical customer cross sell scenario. Most organizations tend to throw (figuratively speaking) the entire product catalog at the customer without any consideration for their lifestage needs, portfolio, demographics etcâ¦ Not only is this a highly ineffective cross sell approach, but it is a terrible customer experience approach. With OneView the customer service representative or the field service agents knows exactly what the customers latest and overall sentiment is, what her product portfolio looks like and which product the customer is most likely to buy.

The end goal of any activity is to make the end customers experience epic. By knowing how a customer is likely to behave, modeled on her previous behavior, insurance companies can ensure that the customer experience is always moving to the right.

Source: A Single Customer View : The Secret Weapon Everyone Must Use by analyticsweek

The Mainstream Adoption of Blockchain: Internal and External Enterprise Applications

The surging interest in blockchain initially pertained to its utility as the underlying architecture for the cryptocurrency phenomenon. Nonetheless, its core attributes (its distributed ledger system, immutability, and requisite permissions) are rapidly gaining credence in an assortment of verticals for numerous deployments.

Blockchain techniques are routinely used in several facets of supply chain management, insurance, and finance. In order to realize the widespread adoption rates many believe this technology is capable of, however, blockchain must enhance the very means of managing data-driven processes, similar to how applications of Artificial Intelligence are attempting to do so.

Today, there are myriad options for the enterprise to improve operations by embedding blockchain into fundamental aspects of data management. If properly architected, this technology can substantially impact facets of Master Data Management, data governance, and security. Additionally, it can provide these advantages not only between organizations, but also within them, operating as what Franz CEO Jans Aasman termed âa usability layer on topâ of any number of IT systems.

Customer Domain Management
A particularly persuasive use case for the horizontal adoption of blockchain is deploying it to improve customer relations. Because blockchain essentially functions as a distributed database in which transactions between parties must be validated for approval (via a consensus approach bereft of centralized authority), itâs ideal for preserving the integrity of interactions between the enterprise and valued customers. In this respect it can âcreate trusted ledgers for customers that are completely invisible to the end user,â Aasman stated. An estimable example of this use case involves P2P networks, in which âpeople just use peer-to-peer databases that record transactions,â Aasman mentioned. âBut these peer-to-peer transactions are checked by the blockchain to make sure people arenât cheating.â Blockchain is used to manage transactions between parties in supply chains in much the same way. Blockchain aids organizations with this P2P customer use case because without it, âitâs very, very complicated for normal people to get it done,â Aasman said about traditional approaches to inter-organization ledger systems. With each party operating on a single blockchain, however, transactions become indisputable once they are sanctioned between the participants.

Internal Governance and Security
Perhaps the most distinguishable feature of the foregoing use case is the fact that in most instances, end users wonât even know theyâre working with blockchain. What Aasman called an âinvisibleâ characteristic of the blockchain ledger system is ideal for internal use to monitor employees in accordance with data governance and security procedures. Although blockchain supports internal intelligence or compliance for security and governance purposes, itâs most applicable to external transactions between organizations. In financeâjust like in supply chain or in certain insurance transactionsââyou could have multiple institutions that do financial transactions between each other, and each of them will have a version of that database,â Aasman explained. Those using these databases wonât necessarily realize theyâre fortified by blockchain, and will simply use them as they would any other transactional system. In this case, âan accountant, a bookkeeper or a person that pays the bills wonât even know thereâs a blockchain,â commented Aasman. âHe will just send money or receive money, but in the background thereâs blockchain making sure that no one can fool with the transactions.â

Master Data Management
Despite the fact that individual end users may be ignorant of the deployment of blockchain in the above use cases, itâs necessary for underlying IT systems to be fully aware of which clusters are part of this ledger system. According to Aasman, users will remain unaware of blockchainâs involvement âunless, of course, someone was trying to steal money, or trying to delete intermediate transactions, or deny that he sent money, or sent the same money twice. Then the system will say hey, user X has engaged in a ‘confusing’ activity.â In doing so, the system will help preserve adherence to company policies related to security or data governance issues.

Since organizations will likely employ other IT systems without blockchain, Master Data Management hubs will be important for âdeciding for which transactions this applies,â Aasman said. âItâs going to be a feature of MDM.â Mastering the data from blockchain transactions with centralized MDM approaches can help align this data with others vital to a particular business domain, such as customer interactions. Aasman revealed that âthe people that make master data management have to specify for which table this actually is true. Not the end users: the architects, the database people, the DBAs.â Implementing the MDM schema for which to optimize such internal applications of blockchain alongside those for additional databases and sources can quickly become complex with traditional methods, and may be simplified via smart data approaches.

Overall Value
The rapidity of blockchainâs rise will ultimately be determined by the utility the enterprise can derive from its technologies, as opposed to simply limiting its value to financial services and cryptocurrency. There are just as many telling examples of applying blockchainâs immutability to various facets of government and healthcare, or leveraging smart contracts to simplify interactions between business parties. By using this technology to better customer relations, reinforce data governance and security, and assist specific domains of MDM, organizations get a plethora of benefits from incorporating blockchain into their daily operations. The business value reaped in each of these areas could contribute to the overall adoption of this technology in both professional and private spheres of life. Moreover, it could help normalize blockchain as a commonplace technology for the contemporary enterprise.

Source: The Mainstream Adoption of Blockchain: Internal and External Enterprise Applications

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

But First! White Labeling vs. Embedding

Three Flexible OEM Architectures

Whatâs The Right Choice?

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

If Youâre Hearing Thisâ¦

Â â¦Then Consider Adding This Analytics Capability to Your Application

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

Nitin had written about stream based data integration on his insightful post titled âCheers to Stream Based Integrationâ

Whatâs The Right Choice?

If Youâre Hearing Thisâ¦

Â â¦Then Consider Adding This Analytics Capability to Your Application

Nitin had written about stream based data integration on his insightful post titled âCheers to Stream Based Integrationâ