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

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[  COVER OF THE WEEK ]

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Data shortage  Source

[ FEATURED COURSE]

Applied Data Science: An Introduction

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As the world’s data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data sets—known as big data…. more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:Given two fair dices, what is the probability of getting scores that sum to 4? to 8?
A: * Total: 36 combinations
* Of these, 3 involve a score of 4: (1,3), (3,1), (2,2)
* So: 3/36=1/12
* Considering a score of 8: (2,6), (3,5), (4,4), (6,2), (5,3)
* So: 5/36

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

 @AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

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[ FACT OF THE WEEK]

By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.

Sourced from: Analytics.CLUB #WEB Newsletter

Everything You Need to Know About Real-Time Business Intelligence

It’s becoming increasingly obvious that the pace at which we create new data won’t slow down anytime soon. With millions of streams and touch points, most businesses create gigabytes of data every minute, yet our rate of data comprehension cannot keep up.

To get the most out of data, companies need to analyze it as soon as it is created—when it can provide the most immediate and relevant insights. Unlike traditional models that look at historical data for patterns, real-time analytics focuses on understanding information as it arrives to help make faster, better decisions. Today, real time business intelligence is a necessity more than a luxury, so it’s important to understand exactly what it is, and what it can do for you.

What is Real-Time Business Intelligence?

Real time business intelligence is the use of analytics and other data processing tools to give companies access to the most recent, relevant data and visualizations. More than anything, this up-to-the-minute information lets organizations make smarter decisions and better understand their operations. To provide real-time data, these platforms use smart data storage solutions such as Redshift data warehouses, visualizations, and ad hoc analytics tools.

Full Stack vs. Data Visualization

To successfully provide you with the best data, real-time BI tools use a combination of server-less analytics (where data is transmitted directly to a dashboard or visualization) and data warehouses. This allows dashboards to show both real-time and historic data in a holistic way. It also lets companies provide users with the data they need to complete their jobs more effectively, and even assists in predictive analytics.

Why is Real-Time BI Crucial for Organizations?

At its core, real-time business intelligence is about understanding data faster and using it to make wise snap decisions. For organizations which produce gigabytes—and even terabytes, in some cases—much of this information loses its relevance once it’s sitting in storage. Information about inventory levels, customer needs, ongoing services, and more can all be incredibly useful, but more so if it’s analyzed as soon as it’s generated.

Real-time analytics and BI also empower users in organizations to perform their own research and use their available data. This includes the ability to perform ad-hoc analysis on existing data or creating visualizations specific to new streams. Finally, real-time BI helps better understand trends and create more accurate predictive models for organizations.

Who Uses Real-Time BI?

There are several industries, sectors, and fields where using real-time BI can optimize an organization:

  • Customer relationship management suites (CRMs) can use real-time data to provide better service for consumers. This includes providing better engagement by tailoring services and conversations to consumers’ known preferences in the moment.
  • Logistics organizations can better understand their supply chains and workflows as they evolve. This allows for better decisions in emergencies (how to best alter a route for faster delivery) or simply to create more efficient chains.
  • Inventory managers can see their stock falling and rising as it happens, letting them make better calls about which products to order. By combining with historic trends, they can also create predictive models for ordering that automate time-consuming tasks.
  • Manufacturers can collect real-time data from machinery and production chains, and see how they are performing, as well as their condition. This allows them to both improve efficiency and productivity and resolve any maintenance issues before they become a full-blown emergency.
  • Risk analysts can take advantage of incoming data to continuously modify their risk models and make better calls on insurance, loans, and a variety of other financial decisions.

What are the Real-Time BI Best Practices?

These are some of the best ways to ensure your real-time business intelligence tools give you the best results:

  • Understand the value of your data – the value of data decreases as it ages. Understand how quickly this change happens and make sure your BI tools optimize your information.
  • Have a strong infrastructure built for your data – Once you collect it, data must be stored, scrubbed, and parsed. Having a clear data warehousing solution and organized data flow can help you optimize your results.
  • Collect the right data – Most importantly, real-time analytics works best when you’re using the most relevant data. Focus on the data that can give you the most pertinent answers for each situation.

Real-time business intelligence is becoming a central aspect of organizations’ decision-making. By implementing the right solution and empowering your teams to use it, you can start making upgrades and finding the best insights in your data.

Full Stack vs. Data Visualization

Originally Posted at: Everything You Need to Know About Real-Time Business Intelligence by analyticsweek

Top 5 Lessons LEGO story teaches an entrepreneur

LEGO logo

LEGO launched a neat video few days back narrating its story. Not only it is nicely done, but also it encapsulates boatload of management and startup lessons. I have listed top 5 lessons that popped out for me in this video. Take a look and feel free to leave your thoughts in comments section. I have included the link to the video at the end of the post. Make sure to give it a pit stop.

 

Here are the five startup lessons:

Keep on iterating:

One thing that was outright clear and motivating is the number of iterations LEGO went through since its inception. It is always great to see companies iterate to stay on top and stay agile.

We could learn to keep our iteration engine cranking to get to a product that works.

 

Persistence pays:

It is one more thing that stood out and is common with most of the big and successful companies. LEGO, even after going through lots of ups and downs, didnot lose its sight and kept on persisting. This is another good lesson to learn. A successful business always takes more time and costs more, but if founder keeps on going, the business will get there. This has been the story of most successful companies today.

 

Have a grand vision:

It was very clear from the video that Ole and then Godtfred has a grand vision of making high quality products for kids. Despite high and low points in their business, they kept their focus and grand vision intact, that is, to serve a revolutionary high quality product. This is another trait of a successful company. It is extremely important to have a grand vision that steers the business in the right direction and prevents it from losing focus from short term impacts.

 

High quality products speak louder and deeper:

From the very beginning, Ole’s focus was to make high quality products. We have seen in the “LEGO story” that word got out and people started recognizing and contacting Ole’s small shop for good quality toys. High quality products go a long way in marketing for a company. High quality products lead to deeper “word-of-mouth” campaigns. If customer enjoy high quality products they keep on sharing the word and build brand loyalists. These loyal customers will work day and night to make sure great products gets to every hand they know. And, wtih current social media channels, the amplification is even greater. So, there is no better marketing than word-of-mouth and with quality product that comes for free.

 

Always listen to your customers:

There was one scene in the story where a kid complains about the inability to move the LEGO creations and that leads to brick locking logic. This is a great example of innovation coming through loyal customers. If companies keep a close eye and ear on their customers, lot of innovation and learning will come free from best users of the products. Most companies create forums and platform to directly connect with their users/customers. Lots of companies like consumer products companies have already mastered this art, but there is still a lot of scope for improvement.

Now for the treat, here is the video:

* logo courtesy wikipedia.org

Originally Posted at: Top 5 Lessons LEGO story teaches an entrepreneur

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

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[  COVER OF THE WEEK ]

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Pacman  Source

[ AnalyticsWeek BYTES]

>> Focus on success, not perfection: Look at this data science algorithm for inspiration by analyticsweekpick

>> List of VC firms in Boston by v1shal

>> Using Big Data In A Crisis: Nepal Earthquake by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. more

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:What is: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
A: Lift:
It’s measure of performance of a targeting model (or a rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. Lift is simply: target response/average response.

Suppose a population has an average response rate of 5% (mailing for instance). A certain model (or rule) has identified a segment with a response rate of 20%, then lift=20/5=4

Typically, the modeler seeks to divide the population into quantiles, and rank the quantiles by lift. He can then consider each quantile, and by weighing the predicted response rate against the cost, he can decide to market that quantile or not.
“if we use the probability scores on customers, we can get 60% of the total responders we’d get mailing randomly by only mailing the top 30% of the scored customers”.

KPI:
– Key performance indicator
– A type of performance measurement
– Examples: 0 defects, 10/10 customer satisfaction
– Relies upon a good understanding of what is important to the organization

More examples:

Marketing & Sales:
– New customers acquisition
– Customer attrition
– Revenue (turnover) generated by segments of the customer population
– Often done with a data management platform

IT operations:
– Mean time between failure
– Mean time to repair

Robustness:
– Statistics with good performance even if the underlying distribution is not normal
– Statistics that are not affected by outliers
– A learning algorithm that can reduce the chance of fitting noise is called robust
– Median is a robust measure of central tendency, while mean is not
– Median absolute deviation is also more robust than the standard deviation

Model fitting:
– How well a statistical model fits a set of observations
– Examples: AIC, R2, Kolmogorov-Smirnov test, Chi 2, deviance (glm)

Design of experiments:
The design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation.
In its simplest form, an experiment aims at predicting the outcome by changing the preconditions, the predictors.
– Selection of the suitable predictors and outcomes
– Delivery of the experiment under statistically optimal conditions
– Randomization
– Blocking: an experiment may be conducted with the same equipment to avoid any unwanted variations in the input
– Replication: performing the same combination run more than once, in order to get an estimate for the amount of random error that could be part of the process
– Interaction: when an experiment has 3 or more variables, the situation in which the interaction of two variables on a third is not additive

80/20 rule:
– Pareto principle
– 80% of the effects come from 20% of the causes
– 80% of your sales come from 20% of your clients
– 80% of a company complaints come from 20% of its customers

Source

[ VIDEO OF THE WEEK]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The temptation to form premature theories upon insufficient data is the bane of our profession. – Sherlock Holmes

[ PODCAST OF THE WEEK]

Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

 Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

By 2020, at least a third of all data will pass through the cloud (a network of servers connected over the Internet).

Sourced from: Analytics.CLUB #WEB Newsletter

Weekly Artificial Intelligence Round-Up – February 22, 2019

Reading Time: 1 minuteHere’s our weekly round-up of the most essential reads on Artificial Intelligence from across the Internet:

AI:It’s just like us: Researchers have made an AI whose performance improves when they let it ‘dream’. They’ve configured a sleep state for the algorithm which cycles through existing information and unlearning unnecessary data, and then consolidating what’s left, the important stuff. This has resulted in higher accuracy scores for the algorithm.

A wealth of AI jobs: According to NASSCOM, India has created 170,000 tech jobs in 2018. The demand for AI, analytics, cybersecurity professionals are in high demand.

AI picks up its pen: Artificial Intelligence is now able to write fiction, but researchers are desisting to open-source the code for fear of misuse. Researchers are concerned that miscreants could use the code to generate large amounts of fake news.  

Elon Musk quits OpenAI: Through OpenAI, Musk used to champion the cause of building a benevolent AI that won’t wipe out our species but has now left the group citing differences with the direction. Musk has not made it clear why he is leaving the group, raising speculations that he might be leaving to spend more time at Tesla and SpaceX.

No Country for Professionals: A top NASSCOM official has stated that the tech industry’s market performance is being affected due to a shortage of 230,000 skilled techies as jobs in AI and Big Data are estimated to be 780,000 by 2021.

Source: Weekly Artificial Intelligence Round-Up – February 22, 2019

The Big Data Debate: Batch vs. Streaming Processing

While data is the new currency in today’s digital economy, it’s still a struggle to keep pace with the changes in enterprise data and the growing business demands for information. That’s why companies are liberating data from legacy infrastructures by moving over to the cloud to scale data-driven decision making. This ensures that their precious resource— data — is governed, trusted, managed and accessible.

While businesses can agree that cloud-based technologies are key to ensuring the data management, security, privacy and process compliance across enterprises, there’s still an interesting debate on how to get data processed faster — batch vs. stream processing.

Each approach has its pros and cons, but your choice of batch or streaming all comes down to your business use case. Let’s dive deep into the debate to see exactly which use cases require the use of batch vs. streaming processing.

Batch vs. Stream Processing: What’s the Difference?

A batch is a collection of data points that have been grouped together within a specific time interval. Another term often used for this is a window of data. Streaming processing deals with continuous data and is key to turning big data into fast data. Both models are valuable and each can be used to address different use cases. And to make it even more confusing you can do windows of batch in streaming often referred to as micro-batches.

While the batch processing model requires a set of data collected over time, streaming processing requires data to be fed into an analytics tool, often in micro batches, and in real-time. Batch processing is often used when dealing with large volumes of data or data sources from legacy systems, where it’s not feasible to deliver data in streams. Batch data also by definition requires all the data needed for the batch to be loaded to some type of storage, a database or file system to then be processed. At times, IT teams may be idly sitting around and waiting for all the data to be loaded before starting the analysis phase.

Data streams can also be involved in processing large quantities of data, but batch works best when you don’t need real-time analytics. Because streaming processing is in charge of processing data in motion and providing analytics results quickly, it generates near-instant results using platforms like Apache Spark and Apache Beam. For example, Talend’s recently announced Talend Data Streams, is a free, Amazon marketplace application, powered by Apache Beam, that simplifies and accelerates ingestion of massive volumes and wide varieties of real-time data.

Is One Better Than the Other?

Whether you are pro-batch or pro-stream processing, both are better when working together. Although streaming processing is best for use cases where time matters, and batch processing works well when all the data has been collected, it’s not a matter of which one is better than the other — it really depends on your business objective.

Watch Big Data Integration across Any Cloud now.

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However, we’ve seen a big shift in companies trying to take advantage of streaming. A recent survey of more than 16,000 data professionals showed the most common challenges to data science including everything from dirty data to overall access or availability of data. Unfortunately, streaming tends to accentuate those challenges because data is in motion. Before jumping into real-time, it is key to solve those accessibility and quality data issues.   

When we talk to organizations about how they collect data and accelerate time-to-innovation, they usually share that they want data in real-time, which prompts us to ask, “What does real-time mean to you?” The business use cases may vary, but real-time depends on how much time to the event creation or data creation relative to the processing time, which could be every hour, every five minutes or every millisecond.

To draw an analogy for why organizations would convert their batch data processes into streaming data processes, let’s take a look at one of my favorite beverages—BEER. Imagine you just ordered a flight of beers from your favorite brewery, and they’re ready for drinking. But before you can consume the beers, perhaps you have to score them based on their hop flavor and rate each beer using online reviews. If you know you have to complete this same repetitive process on each beer, it’s going to take quite some time to get from one beer to the next. For a business, the beer translates into your pipeline data. Rather than wait until you have all the data for processing, instead you can process it in micro batches, in seconds or milliseconds (which means you get to drink your beer flight faster!).

Why Use One Over the Other?

If you don’t have a long history working with streaming processing, you may ask, “Why can’t we just batch like we used to?” You certainly can, but if you have enormous volumes of data, it’s not a matter of when you need to pull data, but when you need to use it.

Companies view real-time data as a game changer, but it can still be a challenge to get there without the proper tools, particularly because businesses need to work with increasing volumes, varieties and types of data from numerous disparate data systems such as social media, web, mobile, sensors, the cloud, etc. At Talend, we’re seeing enterprises typically want to have more agile data processes so they can move from imagination to innovation faster and respond to competitive threats more quickly. For example, data from the sensors on a wind turbine are always-on. So, the stream of data is non-stop and flowing all the time. A typical batch approach to ingest or process this data is obsolete as there is no start or stop of the data. This is a perfect use case where stream processing is the way to go.

The Big Data Debate

It is clear enterprises are shifting priorities toward real-time analytics and data streams to glean actionable information in real time. While outdated tools can’t cope with the speed or scale involved in analyzing data, today’s databases and streaming applications are well equipped to handle today’s business problems.

Here’s the big takeaway from the big data debate: just because you have a hammer doesn’t mean that’s the right tool for the job. Batch and streaming processing are two different models and it’s not a matter of choosing one over the other, it’s about being smart and determining which one is better for your use case.

The post The Big Data Debate: Batch vs. Streaming Processing appeared first on Talend Real-Time Open Source Data Integration Software.

Source: The Big Data Debate: Batch vs. Streaming Processing

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

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[  COVER OF THE WEEK ]

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Insights  Source

[ AnalyticsWeek BYTES]

>> Big Data Introduction to D3  by v1shal

>> Aug 17, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Exploring the Structure of High-Dimensional Data with HyperTools in Kaggle Kernels by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ 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]

Understanding #Customer Buying Journey with #BigData

 Understanding #Customer Buying Journey with #BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

 Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts.

Sourced from: Analytics.CLUB #WEB Newsletter

You’re Invited: Data Monetization Workshop 2018

Juice is proud to announce that it will host the third annual Data Monetization Workshop on Thursday, March 29, 2018 at the Nashville Technology Council’s Tech Hill Commons. Created by local data expert Lydia Jones in 2016, the Data Monetization Workshop brings together some of the top data and analytics practitioners in the country to discuss how to deploy data monetization in a business setting.

This year’s workshop will feature speakers and panelists from companies such as BuildingFootprintUSA, Crystal Project Inc., Dawex, Digital Reasoning, and Uber. The topics covered will be: 

  • The Now: What are the opportunities and business models you should consider to monetize your data? E.g. enhancing existing products, new data products, data marketplaces.

  • The Future: How will emergent technologies such as IoT and AI unlock new opportunities and challenges for data monetization?

Prior to the workshop, attendees will have the option to attend a data storytelling seminar led by Juice Analytics employees. The seminar will showcase Juice’s unique method for quickly and easily creating data stories from a given data set. The workshop will conclude with an open bar networking event.

Attendees in the past have come from Florida, Texas, New York, Georgia, California, Canada, and Australia, and across industries such as healthcare, finance, retail, technology, and government. They are typically members of the C-Suite (including CEOs, CMOS, CFOs, CXOs, and CAOs) as well as data and analytics leaders, data scientists, investors, and data product development leaders, among others.

To learn more about this year’s Data Monetization Workshop, visit the link below. Please be sure to register in advance as seating is limited. We hope to see you there!

 

Learn More & register

Source by analyticsweek

Introduction to AzureKusto

By Hong Ooi and Alex Kyllo

This post is to announce the availability of AzureKusto, the R interface to Azure Data Explorer (internally codenamed “Kusto”), a fast, fully managed data analytics service from Microsoft. It is available from CRAN, or you can install the development version from GitHub via devtools::install_github("cloudyr/AzureKusto").

AzureKusto provides an interface (including DBI compliant methods for connecting to Kusto clusters and submitting Kusto Query Language (KQL) statements, as well as a dbplyr style backend that translates dplyr queries into KQL statements. On the administrator side, it extends the AzureRMR framework to allow for creating clusters and managing database principals.

Connecting to a cluster

To connect to a Data Explorer cluster, call the kusto_database_endpoint() function. Once you are connected, call run_query() to execute queries and command statements.

library(AzureKusto)

## Connect to a Data Explorer cluster with (default) device code authentication
Samples <- kusto_database_endpoint(
server="https://help.kusto.windows.net",
database="Samples") res <- run_query(Samples,
"StormEvents | summarize EventCount = count() by State | order by State asc") head(res) ## State EventCount ## 1 ALABAMA 1315 ## 2 ALASKA 257 ## 3 AMERICAN SAMOA 16 ## 4 ARIZONA 340 ## 5 ARKANSAS 1028 ## 6 ATLANTIC NORTH 188 # run_query can also handle command statements, which begin with a '.' character res <- run_query(Samples, ".show tables | count") res[[1]] ## Count ## 1 5

dplyr Interface

The package also implements a dplyr-style interface for building a query upon a tbl_kusto object and then running it on the remote Kusto database and returning the result as a regular tibble object with collect(). All the standard verbs are supported.

library(dplyr)
StormEvents <- tbl_kusto(Samples, "StormEvents")
q <- StormEvents %>%
    group_by(State) %>%
    summarize(EventCount=n()) %>%
    arrange(State)
show_query(q) ## database('Samples').['StormEvents'] ## | summarize ['EventCount'] = count() by ['State'] ## | order by ['State'] asc
collect
(q) ## # A tibble: 67 x 2 ## State EventCount ## ## 1 ALABAMA 1315 ## 2 ALASKA 257 ## 3 AMERICAN SAMOA 16 ## ...

DBI interface

AzureKusto implements a subset of the DBI specification for interfacing with databases in R.

The following methods are supported:

  • Connections: dbConnect, dbDisconnect, dbCanConnect
  • Table management: dbExistsTable, dbCreateTable, dbRemoveTable, dbReadTable, dbWriteTable
  • Querying: dbGetQuery, dbSendQuery, dbFetch, dbSendStatement, dbExecute, dbListFields, dbColumnInfo

It should be noted, though, that Data Explorer is quite different to the SQL databases that DBI targets. This affects the behaviour of certain DBI methods and renders other moot.

library(DBI)

Samples <- dbConnect(AzureKusto(),
                     server="https://help.kusto.windows.net",
                     database="Samples")

dbListTables(Samples)
## [1] "StormEvents"       "demo_make_series1" "demo_series2"     
## [4] "demo_series3"      "demo_many_series1"

dbExistsTable(Samples, "StormEvents")
##[1] TRUE

dbGetQuery(Samples, "StormEvents | summarize ct = count()")
##      ct
## 1 59066

If you have any questions, comments or other feedback, please feel free to open an issue on the GitHub repo.

And one more thing…

As of Build 2019, Data Explorer can also run R (and Python) scripts in-database. For more information on this feature, currently in public preview, see the Azure blog and the documentation article.

 

 

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

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Artificial Intelligence

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A: * When the number of features is large comparing to the number of observations (e.g. document-term matrix)
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