fabiochiu/medium-articles huggingface dataset:
THE TRIANGLE OF HATE
By: Nergis Celenk
Tags: Fear, Machiavelli, Love, Hate

It is maintained that people need to empathize to be ruled because they need to feel valuable for the Prince but humankind is not that docile and simple. Machiavelli stated that: Humankind be there for you when everything is peaceful but they leave you when danger is come (Machiavelli, 2019 , p.57). As a result of this danger, there is no chance as the Prince known as merciful after the action he took. The ruler should not be worry thought cruel by the people. Treating merciful might be more cruel. Being cruel is beneficial for the Prince at the point of ruling the people. Machiavelli provided that it is more cruel if he accept the war and mutinty just because he does not want to known as cruel because it affects the community, but the choice of prince does person (Maciavelli, 2019, p.57). It is acceptable that people thought that they have a prince who is cruel in risky situation. The real reason why the Prince does not treat to the people and his army better will save the State. If there is a chance, the fear should be choosen.

There is a thin line between being loved or being hatred. For the ruler of army that distinction has more significant role on ruling. According to modern century tecnique, kindess is one major key to create connection between the ruler and the people. But being kind on the troop which is hard to kept united such as army does not effective. Machiavelli said that: “ Men are less hesitant about offending or harming a ruler who make himself loved than one who inspires fear. Fear is sustained by a dread of punishment that is always effective” (Machiavelli, 2019, p.58). Armies never prepared for military action if their ruler is not to be harsh. Hannibal formed soldier from different nationalities and make them united aganist to the enemy, never appeared any problem. It can be explain with the cruelness of him which make him respected and feared by his army. Kindness might be the solution for daily human relationship but being feared is needed for special situation.

In conclusion, the Prince should find the balance of being loved or hatred while he showing his cruelness. The Prince needs to make himself feared in such a way that, either the people should not love and hate him. The best way of that using poker face to the people.
Hello everyone I geuss this is my first time doing this my name’s summer Im not the very brightest…
By: Summer M
Tags: Fear, Peace Of Mind, Life Lessons, Self Worth, Love

Hello everyone I geuss this is my first time doing this my name’s summer Im not the very brightest person but life is fair life is a gift that some people wish they didn’t receive but some people have it worse the blessings or little moments you have with someone or something thing which is predicted on your pronouns I hope that you will have very few bad days but I will get hard sometimes I will be here for you listen and read maybe I will help you over come your fears take advise but don’t take it to heart it can’t always help lead with heart and mind though
A Stoic’s Guide to Becoming Fearless
By: Adrian Drew
Tags: Philosophy, Fear, Self Improvement, Self, Personal Development

Born a slave in ancient Turkey, Epictetus made vast contributions to the fields of philosophy and literature later in his life. He taught many important lessons, often declaring philosophy as a way of life rather than a theoretical discipline.

Epictetus studied philosophy with the permission of his owner, allowing him to gain respect as he ascended into adulthood. During his older years, he composed several famous writings surrounding peace of mind, happiness, and most importantly, fear.

Epictetus held that all external events are beyond our control, even if that might not always appear to be the case. In order to be truly happy, we must learn to calmly and dispassionately accept whatever happens to us.

In his words,

“Men are disturbed not by things, but by the view which they take of them.”

Happiness doesn’t exist outside of ourselves, but rather in our thoughts and beliefs about that which happens to us. Fear is the root of almost all of our pain, and freedom from fear, or resistance to it, is critical to satisfaction in our work, lives, and relationships.

Freeing ourselves from anxiety, however essential, isn’t always easy. Where do we start?
Infer Schema for Large CSV File Hosted in AWS S3
By: Anatoly Danilov
Tags: Csv, Spark, Aws S3

Problem

To infer schema in AWS we can set a special option that works pretty well for small to medium file sizes:

.option("inferSchema", "true")

It takes one extra parsing run through the whole file before actually processing it. You do not really want that given slow connection and when you really want to just peek on the output

Solution

We will cut off the first 1000 records and will hope that this is enough to give a hint to a recipient about the schema. Do the full run if you need 100% surety about the schema upfront.

To access your bucket in S3 set the Hadoop configuration properties

// set access credentials for S3

val hadoopConfiguration = sparkSession.sparkContext().hadoopConfiguration()

hadoopConfiguration.set("fs.s3a.access.key", "<key>")

hadoopConfiguration.set("fs.s3a.secret.key", "<secret>")

What we are doing is reading a CSV as a text file and creating a small dataset out of it. Then using the magic of Spark to infer the schema from it:

Conclusion

We have seen how to infer a schema from a portion of a CSV file by using an intermediate dataset of strings. This can be useful in situations when you want to peek at the schema before starting a long-running task upon it.
Portable Pipelines with Apache Beam
By: Dennis De Weerdt
Tags: Apache Flink, Big Data Pipeline, Spark, Apache Beam

There are lots of use cases for data processing and analytics pipelines, and nearly as many frameworks to use. Apache Spark is probably the de facto standard these days, but it is far from the only option. In this article, we'll take a look at Apache Flink — one such alternative — , and more importantly Apache Beam, which will make it so you don't have to worry about picking the right framework ever again.

First off, Flink. At first glance, Spark and Flink seem very similar (as do some of the other frameworks). Flink is a lot newer, though, and its main distinguishing feature is the fact that it is based on stream processing, rather than batch processing. It can still emulate batch processing if required, but fundamentally it works with streams. This is in sharp contrast to Spark, which uses batches and can emulate streams with so called micro-batches. Spark Structured Streaming does now also have a continuous processing mode, but in the most recent version at the time of writing (Spark 3.0.1), it remains experimental and unlike Flink does not offer exactly-once guarantees.

The choice of which framework to choose can be pretty difficult, then. Fortunately, Apache Beam comes to the rescue. Beam is not a data processing framework, but rather a unified programming model for data processing tasks. It ships with a number of runners capable of executing tasks using Spark, Flink, Google Cloud Dataflow, and a variety of other tools. That means you can implement your ETL logic once, and run it on any of the supported pipelines while only needing to change some command line parameters.
Too Small Data — Solving Small Files issue using Spark
By: Saurav Agarwal
Tags: Spark, Small Files

I am pretty sure you all must have come across the issue of Small files issues while working with Big Data frameworks like Spark, Hive etc.

What is a small file?

Definition of small file can be a data file which is considerably smaller than the default block size of the underlying file systems (e.g. 128MB by default in CDH)

Sources of small files?

These small files originate from different sources of data as well as resultant of data processing jobs as well.

Problems due to small files

In addition to creating inefficient storage (particularly in HDFS etc) mainly, small files affect the compute performance of the job a lot as well. The reason being small files results in more disk seeks while running computations through these compute engines.

For example, we know that in Spark, the task within an executor reads and processes one partition at a time. Each partition is one block by default. Hence, a single concurrent task can run for every partition in a Spark RDD. This means that if you have a lot of small files, each file is read in a different partition and this will cause a substantial task scheduling overhead compounded by lower throughput per CPU core.

Solution

The solution to these problems is 3 folds.

First is trying to stop the root cause.

Second, being identifying these small files locations + amount.

Finally being, compacting the small files to larger files equivalent to block size or efficient partition size of the processing framework.

For avoiding small files in the first place make sure the processes producing the data are well-tuned. In case the process is Spark make sure you use repartition() OR coalesce() OR set the spark shuffle partitions property effectively to avoid small files issue in the first place.

If things are already worse. Now, let's try to understand how to identify these small files. We will consider HDFS as the storage framework here.

Below shell script can be run using all list privileges in the base path on which small files need to be identified.

Now that final_list.csv has a list of the directory at N depth_level and the respective amount of small files in it sorted by the amount in decreasing order, we can very well run a Spark job to concatenate these small files as an offline process. Below is a snippet of the same.

This code snippet written in Scala will merge the small files into larger files by dynamically calculating the size of the target directory using the bucket_cal function.

Hope it will help, do let me know in case of any questions.
SparkoCam Crack v2.7.2 With Full Torrent Key Free Download
By: Luke David
Tags: Sparkocam Crack, Sparkocam, Sparkocam Key, Spark, Sparkocam Serial Number

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CCA-175 Cloudera CCA Spark and Hadoop Developer
By: Ankit Malik
Tags: Cloudera, Certification, Spark, Spark Sql, Cca175

Exam Focus Area

The exam (since 2020) focuses completely on Spark (2.x) and there are no questions on Sqoop, if you see sqoop questions in any of the online trainings or practice exams just ignore them completely.

The areas you need to focus on for the exam are -

Reading and writing files to HDFS (all types — text, csv, avro, parquet etc.), I mean ALL Spark Data Frame Spark SQL

If you study all these things smartly then you can crack the certification in 1–2 weeks of preparation.

Exam Preparation

My personal preparation was a bit different than what I would recommend anyone to do, so let me tell what I had studied regarding spark. It also speaks to my knowledge rather than the certification preparation.

I had done the cloudera on-demand training in 2018 (sponsored by my organization). When I decided to appear for the exam, I did a Udemy course by our trusted Frank Kane in september 2020. Due to an organization engagement, I did the on-demand training again in October 2020. Completed the Udemy course and took the practice test in November 2020.

I know…I know…this was probably an overkill. I wont recommend anyone to do what I had done, may be I was enjoying exploring spark. Anyway, here’s what you should do -

Complete the Udemy course Take the practice test

If you have relevant spark experience you should be able to finish the above in 1–2 weeks. If you are confident enough perhaps because you work on spark daily, then I would suggest you do the practice test directly. If you score 100% in the test you will probably clear the exam without breaking a sweat.

Things to take care before appearing for the Exam

The exam although performance based is not at all difficult, the questions are predictable and you can find practice tests for free which are pretty close to the actual one.

What matters in the exam is the timing because the environment is dead slow. If the examiner does not allow you to connect a second monitor then you probably have to deal with a small laptop screen split into terminal window and mozilla (for questions) coupled with a dead beat VM. The VM slows you down considerably so practice the test mentioned above and you should be able to complete that in 1 hour 30 minutes to accommodate the delay in the actual exam.

You don’t have to import any packages, everything is available in the environment.

All the best to everyone appearing for the exam. Feel free to drop any comment or feedback.
Kubernetes 에서 Spark 배치 쿼리로 HDFS 없이 S3 에 기록하기
By: Jung-Taek Lim
Tags: Kubernetes, S3, Spark, Magic Committer

Apache Hadoop Amazon Web Services support - Committing work to S3 with the S3A Committers

This page covers the S3A Committers, which can commit work directly to an S3 object store. These committers are…
a spark
By: Aryana N.F.
Tags: Reality, Spark, Depression, Mental Health, Journal

You know when you’re feeling really low, for like, a really long time and you finally feel that spark of motivation or inspiration?

I haven’t felt that spark in too long. On occasion I have some motivation to do things, but it’s always for other people and never for myself. The only things I do are in the drive to seem normal to others. So that I don’t seem like I’m struggling.

I found inspiration in sending letters to my friends for Christmas. It’s been absolutely heavenly feeling that drive again, but I just know it’s ending. With what seems to be a curse on my very existence, no doubt, all of this spark will vanish and I’ll forget the feeling again.

I don’t even have a drive to keep going on, I’m only here for others. I find myself helping others and taking care of others more than I do for myself. Hell, I don’t even focus on my own pain anymore; I’m merely coasting by, forgetting to worry about what’s really wrong and focusing on others. Every time I’m feeling low, something happens to a friend or a family member and they almost always come to me to talk. It’s a blessing, really, because at least I can prevent them from feeling the same way that I do.

But it still hurts. Everything I’ve been put through is like a constant kick in the back, forcing me deeper into the mud. Every time I’m reminded, it’s like I’m suffocating yet also floating. I don’t even recall most of the feelings I felt during my hardships, but they also come back in full swing. They’re always so temporary but I’m always so thankful. I don’t know what I’d do if I had to feel them for longer periods of time.

This lack of really living is damaging me and I honestly have no drive to try and become healthier. I’m an adult now and I don’t even know how to properly take care of myself. It’s rough. But I find comfort in being there for other people more than myself because again, at least I can prevent them from feeling the same way that I do.

Maybe someday I can feel that spark again. I know it’s pointless to really write on this where no one may even see this. But I have a little drive now writing this and possibly finding people that can relate.

Your depression won’t be forever. Your trauma is a temporary pain. Please, drink some water and eat something healthy, take care of yourself for me.
Как оптимизировать запрос в Apache Spark SQL: Predicate Pushdown vs Projection Pushdown
By: Nick Komissarenko
Tags: Pyspark, Spark, Bigdata

Dell EMC Certified trainer since 2007, co-founder of Big Data School - delivering Big Data, Data Mining, Hadoop, NoSQL, Kafka, Spark cluster courses in Moscow!
Installing and using Spark from Jupyter in three steps on Windows 10
By: Baddy Mkh
Tags: Anaconda, Jupyter Notebook, Spark, Pyspark

*Method 2: in your Command Prompt type the next command to check the installed Java version. However, be sure that you are in the correct Java path otherwise it will not work for you so go to your Java.exe file using:

cd your java path

java -version # after finding your Java.exe type this

If Java is installed you will got like this

If Java is not installed you will get the next sentence

‘java’ is not recognized as an internal or external command, operable program or batch file.

All what you need to do is visiting the next site and following the indicated instructions, it is easy to install: https://www.java.com/it/download/

After installing close and re-open the Command Prompt to re-check the version!! (Pyspark requires 7 or later)

2.2- Apache Spark

Go to the next site and download hadoop spark: https://www.apache.org/dyn/closer.lua/spark/spark-3.0.1/spark-3.0.1-bin-hadoop2.7.tgz

Here it is not required to run any installer all what you need to do is to unzip the files from the downloaded spark-3.0.1-bin-hadoop2.7.tgz folder. You can create a folder called Spark (for example) in your workspace and put all unzipped files there.

The folder path will be like : C:\Users\your_name\Desktop\Spark\spark-2.4.0-bin-hadoop2.7 or you can create this Spark folder under C: in this case your path would be like : C:\Spark\spark-3.0.1-bin-hadoop2.7

I would recommend to copy this folder where all project using Pyspark exist and be sure that the folder path or name does have spaces.

Now you need to add the Spark_Home to your system environmental variable:

1- Right click on Computer and choose Properties

2- Choose the Advanced system settings then click on Environment Variables.

3- Add the variable Spark_Home under the section System Variables :

3.1- Click new and in the name space type Spark_Home

3.2- In the value space type : C:\spark\spark-3.0.1-bin-hadoop2.7 or C:\Users\your_name\Desktop\Spark\spark-2.4.0-bin-hadoop2.7 (depends on your path value)

To verify if Pyspark is installed successfully open Anaconda prompt and type :

Cd Spark_Home\bin and then type pyspark you must get a message like the one bellow (in the picture)

Spark installation is successful

Here our Spark version is 3.0.1 to exit type exit()
Spark Job Server — Active Context Detection
By: Akhil Pillai
Tags: Sparkjobserver, Spark

Spark Job Server :

spark-jobserver provides a RESTful interface for submitting and managing Apache Spark jobs, jars, and job contexts When we submit a spark application on a Spark cluster, the life span of the spark context is till the end of the Spark Application. Reference link for SparkContext :https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/SparkContext.html While in SJS we can re-use the spark context. It provides REST API to create/delete the spark context, the same context can be used for submitting multiple jobs. For example : we want to load multiple tables from S3, so we will create a spark context for S3, this context will be re-used for all the load jobs. Once we are done with all load jobs we will call the delete API to delete the context.

Reference link :

https://github.com/spark-jobserver/spark-jobserver/blob/master/doc/submitting-jobs.md

Problem Statement :

On a resource constrained environment, while submitting jobs on Spark Cluster via SJS to load data from different Data Sources, we have to clean up the old contexts to create room for the incoming Spark Applications.

But if we blindly delete the old contexts, the running jobs if any within the context will fail.

Active Context : if any job is running within a context , let’s call it as an active context

The existing REST APIs exposed by SJS doesn’t have support to fetch the Active Contexts. So if we call the Context Delete API for an active context , all the running jobs in that context will fail.

The work around is to fire a Query to the SJS backend and check if there are any jobs in RUNNING state, if not then only call the context delete API .

But this is not a cleaner approach in the micro services architecture where “Service A” directly connects to “Service B’s” backend

Solution :

This problem can be solved by exposing the following rest APIs:

API to fetch all the active contexts. API to check if a given context is active

Before calling the context delete API , we can cross check using the above API to detect the active contexts.

Code :

https://github.com/akhil741990/spark-jobserver/tree/feature/active-context-detection

Reference Links : https://github.com/spark-jobserver/spark-jobserver

Existing APIs : https://github.com/spark-jobserver/spark-jobserver#api
Kubernetes 에서 Spark 어플리케이션 실행하기 (Kafka helm chart 설치 포함)
By: Jung-Taek Lim
Tags: Kubernetes, Spark, Kafka

Pull an Image from a Private Registry

This page shows how to create a Pod that uses a Secret to pull an image from a private Docker registry or repository…
Revise your PySpark knowledge
By: Brijesh Rana
Tags: Python, Spark, Pyspark, Big Data, Data Science

Photo by Maxwell Nelson on Unsplash

As a Data Engineer you might have to switch between different programming/scripting languages after certain period. In my experience I usually need to switch between pure SQL queries, Java, Shell scripting, Scala and Python as per the project requirements.

These immediate transitions may take some time to get us on-board, sometimes we need to look for whether the direct functions are available for that programming language or not along with the syntaxes. So I am writing down some key points or we can call them `Cheat Codes` for PySpark which may become helpful in your transition to PySpark after a long break.
Introdução ao Spark — Parte 1 by Tiago Diniz
By: Tiago Diniz
Tags: Analytics, Big Data, Spark, Big Data And Analytics

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
SparkoCam Crack v2.7.2 With Full Torrent Key Free Download
Tags: Sparkocam Key, Spark, Sparkocam Serial Number, Sparkocam Crack

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Understanding Customer Churning using Spark for Big Data
By: Erick Ramírez
Tags: Udacity Nanodegree, Churn Prediction, Spark, Machine Learning

The demand for audio streaming services has been increased, the offer for these services has increased too, and the competence is fierce. Customer loyalty to a specific app or service is crucial for this model business.

photo by https://unsplash.com/@blocks

Sparkify is a fictional popular digital media service created by Udacity, similar to Spotify or Pandora; many users use their services every day. It also has two modalities: using a free tier or a premium subscription model on which a user is free to upgrade, downgrade or cancel the service as they will like. It is important that the user likes the service to be loyal to Sparkify.

We track each event; if a user plays a song, visits a specific page, or gets an error, it generates a lot of data that will easily scale up to GB of data. Fortunately, for large amounts of data, we can use big data technologies like Spark, and we can analyze and predict customer churn to avoid that they leave.

What is customer churn?

Is when a user stops using our company’s product, in this case, the Sparkify service. It is a metric that helps if the business is growing or not, and it is important because normally it costs more to acquire new customers than it does to retain the existing ones. To reduce customer churn, Sparkify has decided to analyze the data and offer incentives to keep the customers.

Project definition

It is a realistic dataset with Spark to engineer relevant features for predicting churn. Using Spark MLlib to build machine learning models with large datasets. The full dataset is 12GB ( s3n://udacity-dsnd/sparkify/sparkify_event_data.json) , of which you can analyze a mini subset 128MB( s3n://udacity-dsnd/sparkify/mini_sparkify_event_data.json )

The code for this project is in the Github repository.

create a spark session:

# create a Spark session

spark = SparkSession \

.builder \

.master("local[*]") \

.config("spark.ui.port",3000) \

.appName("Sparkify") \

.getOrCreate()

Load and Clean Dataset

for the mini subset of data, it is a small dataset for Machine learning; it contains 286,500 rows and the following columns:

list of columns of the dataset

there are missing userIds, normally the cookies are disabled, or we cannot track that specific user, then the data related to them has been removed. There are 225 unique users, which has the following distribution:

Customer count free/paid distribution by gender(F/M) and Churn (cancelled)

Exploratory Data Analysis

We have defined as churn the visit to Cancellation Confirmation page and the data have been evaluated according to it if the user has churn or not.

23.11% of users have canceled the service, and the user interactions about the visit pages are the following:
Interview with Ash Herr
Tags: Festivals, Spark, Workshop, AR, Art

Interview with Ash Herr

I’m a multimedia artist and creative technologist from Texas! I moved to San Francisco after university and started heading towards the intersection of art & technology. Haven’t looked back since.

What is your definition of art + tech?

fluid!

Who, or what, inspires you in your art and in your life?

I love art that enhances our perceptual information and sensory modalities — like how behavior can shift in a certain setting, how energy moves around us, how we change over time.

What’s next for you as an artist?

I’m working on several interactive installations! stay tuned :~)

Are there any technologies that you are excited about working with or that have been a game changer for you?

I have really enjoyed working in augmented reality! i think augmented reality is an incredible medium for artists, especially as it facilitates the breaking down of boundaries between our natural and artificial realities.

What’s the best piece of advice you’ve been given?

The Ira Glass quote on the taste gap — “Nobody tells this to people who are beginners, I wish someone told me. All of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple years you make stuff, it’s just not that good. It’s trying to be good, it has potential, but it’s not. But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you. A lot of people never get past this phase, they quit. Most people I know who do interesting, creative work went through years of this. We know our work doesn’t have this special thing that we want it to have. We all go through this. And if you are just starting out or you are still in this phase, you gotta know it’s normal and the most important thing you can do is do a lot of work. Put yourself on a deadline so that every week you will finish one story. It is only by going through a volume of work that you will close that gap, and your work will be as good as your ambitions. And I took longer to figure out how to do this than anyone I’ve ever met. It’s gonna take awhile. It’s normal to take a while. You’ve just gotta fight your way through.”

You can meet Ash Herr at the CODAME ART+TECH Festival『 JOYNT 』
Spark ParserModes comparison
By: Anatoly Danilov
Tags: Csv, Exception Handling, Spark

FailFast, Permissive, and Drop Malformed all behave differently

Hard choice!

Input

For the use cases here and below we will use this CSV file:

We can spot that for the two header columns row 4 and 6 have extra separators thus will break our parsing 🚫

Spark will be told to use schema age STRING, listen_mom STRING which definitely should cause some troubles, let’s see how different modes parse it.

FAILFAST mode

In this mode we expect an exception to be thrown on the first malformed record

Yes, Spark and Kotlin — friends forever

And there we go, Spark recommends us to use PERMISSIVE mode!

That’s called `die trying!`

PERMISSIVE mode

This mode will output you something though

Based on your business case you can decide if that’ allowed or not at all

This mode had dropped some portion of a file that didn’t match the schema

DROPMALFORMED mode

Use it when you want to be sure no one gets bad data for you

Spark says — Spark does! it had dropped the values which didn’t comply with the schema

Conclusion

Use parse mode wisely
OMGFIN Exchange Will Support the Spark (SPARK) Airdrop Program for XRP (XRP) holders
By: Omgfin Exchange
Tags: Ripple, Spark, Xrp, Airdrop

OMGFIN Exchange Will Support the Spark (SPARK) Airdrop Program for XRP (XRP) holders Omgfin Exchange ·Dec 11, 2020

OMGFIN Exchange Will Support the Spark (SPARK) Airdrop Program for XRP (XRP) holders

When OMGFIN performs the snapshot on users’ XRP (XRP) holdings for the Spark (SPARK) airdrop, it will only count XRP positions in spot wallets, savings accounts, and coin-margined futures wallets. XRP holdings in margin accounts and crypto loans will be excluded.

Note: XRP that was borrowed from the margin and crypto loans, even transferred to spot wallets, savings accounts, or coin-margined futures wallets will not be counted as part of the position.

Thanks for your support!

OMGFIN Team
Demystifying Spark’s Stream-Stream OUTER Join
By: Tony Liu
Tags: Streaming, Big Data, Spark, Data Science

Photo by Francisco Gonzalez on Unsplash

Real-time analysis & recommendation is always a fancy idea in data science. Nothing is more exciting than serving the most up-to-date machine learning prediction to our beloved users, right? One way to achieve real-time is to use a spark stream job, write to a low-latency DB as new data comes in. Often times, we want to enrich that new user data with some metadata or even recommendations, we can either create a UDF to call an external API based on the unique user identifier to pull the data, or have that enriching data ready as a table to join back. If we take the latter approach, we end up with two choices. Either we have a static table, which is pre-computed and loaded at the start of your service, the downside is if you want to refresh that table with new recommendations, you need to restart the service because a table(DataFrame) in Spark is immutable and if you re-use that table more than twice, Spark will automatically cache it for you, even if you try everything to unpersist it. This is how Spark’s DAG works internally. The other option is to make that static table a streaming one, meaning you write the new recommendation somewhere and watermark it and ask Spark to pick the new data as a stream. This is called stream-stream join in Spark. Everything is not that easy as it appears when it comes to if you want to do an Outer join.

I have spent a whole day on the second choice and try to understand how outer-join outputs results. Yes, you heard it right, I literally spent a whole day figuring out how left-join works. Crazy, right? But consider the problem setting, we are dealing with streaming data, streaming makes everything a lot harder. I will talk about some issues that I saw along the way and post some code and results at the end.
With a goal to split an RDD 75–25 , I applied randomSplit() with no seed specified in it.
Tags: Big Data, Spark

I now understood that it might be because the underlying RDD wasn’t cached. So, on each partition, the split varied.

Please correct me if I am reading this wrong.
I Love a Manipulative Pisces
By: Lindsay Seligman
Tags: Manipulate, Lovely, Spark, Pisces, Joy

November 25th, 2020

I love a manipulative Pisces. Easy to talk to and be around.
Predicting Churn for Music Streaming App With Spark
By: Jerry Ziyuan Yan
Tags: Spark, Data Science, Python, Machine Learning

Our dataset also provides location attributes and the bar graph below shows the number of churned customers by various cities.

Image by Author

Finally, let’s look at some interesting stats, here are the top 20 popular songs that users listened to. <You’re the One> is obvious the crowd favorite.

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Data Engineering

To better prepare our data for modeling, we need to do some feature engineering. Remember the principle: garbage in garbage out! Also when writing the script, we want to make sure the script is scalable and modular.

One of the examples of such practice is using pipelines. Here I used the pipeline to transform categorical variable location and gender data to the numeric form because most of the current ML algorithm requires a numeric vector. The pipeline will help us streamline our fit and transform process and make the code looks clean and easy.

Image by Author

Modeling

Here comes the main entree, we have all the data ready for modeling. Before we build any model, we need to make sure we split the full dataset into the train, test sets. After that, we can start materializing our model. I also used a function to make thing looks nice.

Image by Author

Here I have tested out several of the most common machine learning methods and evaluated the accuracy of the three models using the F1 Score. Since the churned users are a fairly small subset, the F1 score should be used to balance the precision and recall.

Tunning the Machine Learning model is also an option in Spark. We can do a grid search here and use cross-validation to identify the best fit. I have put an example in the cells below.

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Validation of the Best Model

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Utilizing grid search, we are able to improve our model accuracy by 10 % F1 score comparing to the base untuned logit model.

Conclusion

In this piece, we walked through how to manipulate a large dataset with Spark in python, apply a data engineering pipeline, and find the best machine learning model. There are a lot of other great things that we can do in the distributed system and this is just the tip of the iceberg. We can of course add in more features in the grid search and increase the parameter intervals. Also, we can try more popular & advanced boosting models that will iteratively look at the training data to help reinforce the model performance.

Happy machine learning and I hope you enjoying reading.

Link to GitHub Repo: https://github.com/jerryanziyuan/Churn-Analysis-with-PySpark
Ripple & Spark token airdrop: a way to make money?
By: Feyyaz Alingan
Tags: Cryptocurrency, Ripple, Airdrop, Spark, Xrp

In this article we will dive into the concept of airdrops, why they are relevant for traders and investors, why the Spark airdrop is being launched on XRP’s infrastructure and how you should approach airdrops both strategically and from an investment perspective.

Swissquote will support the Spark token airdrop for XRP users

Airdrops are a way for projects to launch on the back of existing cryptocurrencies in order to have a bigger user base from the start. If you start from zero, you have to take care of everything, from the technological implementation to marketing the project to the right people. With an airdrop, you can save your resources by giving away coins to an existing user base, which already roots for the existing cryptocurrency that they are holding in their wallets.

The concept of airdrops is nothing new. Back in 2017 many ICO projects did distribute some coins in form of an airdrop to the holders of the respective cryptos. OmiseGo (now called OMG Network) was originally an ERC20 Token, based on the Ethereum blockchain. While they did a rather successful ICO, they still decided to give away 5% of the coins in form of an airdrop to people holding at least 0.1 ETH in their wallets.

Trading airdrops can be quite profitable, as OmiseGo has reached up to 24 USD in the bullrun and was essentially given out for free to those existing Ethereum users.

Something similar happened with the UNI token that was distributed to all users of the Uniswap platform a couple of months ago. The 400 distributed UNI were at the peak worth ~3500 USD, a formidable trade even if you didn’t get the airdrop but traded it from its start price of around 3 USD!

Source: piiggy.com

Why XRP?

But why is the Spark airdrop being launched on XRP? Why not like the other 77% of the airdrops which launch as ERC20 tokens on the Ethereum platform?

Flare Networks, the creators of the Spark token, had a vision: Make 75% of the value that is inside public blockchains available in a trustless manner with smart contracts.

While XRP is one of the fastest cryptocurrencies, it doesn’t have smart contract functionality like Ethereum does, excluding all XRP holders from participating in the on-going trend of DeFi (Decentralized Finance) products, which are 99% based on Ethereum products.

At the same time, XRP does have a strong enough user base, with a fairly cheap price tag for its coins (for retail investors this can be a big psychological factor entering a market!). This combination made Flare go with XRP.
XRP Owners Are About To Get Their Spark Tokens
Tags: Airdrop, Spark, Xrp

coinputin.com — XRP, as it is known, has recently experienced a big upswing. The rally, which saw an increase of almost over 200%, gave XRP owners hope.It is claimed that what has helped push XRP to current levels while this rise is being built is also the promise of Flare AirDrop and free Spark token.



Flare airdrop “snap shot”for XRP holders will take place later this week, and there are only a few days left to prepare your wallet, request Spark tokens. So are you ready for airdrop?



Why did Flare AirDrop trigger XRP explosion?XRP investors experienced a more painful bear market than most other crypto investors and suffered major woes after the market’s worst performance for three years. With each drop in the price of XRP, the army of investors also began to psychologically collapse. Those who have managed to hold on, however, are expected to return to the top 4 of 2020. he was well rewarded with a surprise rally of 200% in the quarter.



In addition to bullish momentum and the resulting return on investment, Ripple owners can claim an equal amount of Spark tokens as part of Flare Network’s upcoming AirDrop. The lure of free tokens, which can only be accessed by holding XRP, was part of what has driven altcoin’s recent rise, according to analysts. That’s why investors don’t want to miss AirDrop for just three days.



How To Get Spark Token?Flare Network uses the XRP Ledger but brings Ethereum smart contracts to run DeFi applications in the protocol created by Ripple. The easi
Use-case example: TF-IDF used for insurance feedback analysis
By: Vojtech Poriz
Tags: Sklearn, Spark, Tf Idf, Information Retrieval, AI

Bag of words

Because machine learning models cannot work with text data directly, we need to convert these responses into some numerical representation. We will start by transforming the text into tokens — individual words.

[ ‘The’, ‘online’, ‘system’, ‘for’, ‘reporting’, ‘insurance’, …] [‘I’, ‘was’, ‘surprised’, ‘by’, ‘the’, ‘speed’, …] [‘I’, ‘paid’, ‘this’, ‘expensive’, …]

Then we create a corpus — set of all the words from the responses.

[‘but’, ‘by’, ‘claim’, ‘declined’, ‘doesn’t’, ‘expensive’, … , ’the’, ‘this’, ‘Unbelievable’, ‘was’, ‘work’, ‘years’, ‘you’, ‘10’]

For each response, we mark the number of occurrences of each word.

Term frequency table of our responses

We have just created a popular text representation — bag-of-words. What’s more, we have also implemented something called TF — term frequency. Each word from the response is weighted by how many occurrences it has. Can we use this to highlight important words? Let’s try to embolden words that have high TF scores.

‘The online system for reporting insurance claim doesn’t work on the phone.’ ‘I was surprised by the speed of resolvement of my insurance claim.’ ‘I paid this expensive insurance for 10 years, but you declined my claim? Unbelievable!’

That doesn’t look very informative nor helpful, does it?

Some words in the responses are clearly more important than others, but by simply counting the term frequencies, we treat words like ‘insurance’, ‘for’, ‘expensive’ and ‘speed’ the same. Of course, the clients are mentioning insurance claims, but we want to highlight the specific problems…

So what do we do about it? We could move all the common words into „stop words“ and ignore them altogether. But this would eliminate too much information and might actually be harmful.

We are smarter than that, we implement TF-IDF!
5 лучших практик работы с кэшем в Apache Spark SQL
By: Nick Komissarenko
Tags: Sparksql, Spark, Bigdata

Dell EMC Certified trainer since 2007, co-founder of Big Data School - delivering Big Data, Data Mining, Hadoop, NoSQL, Kafka, Spark cluster courses in Moscow!
Hadoop Evolution & Architecture
By: Narayana Basetty
Tags: Hadoop, Spark, Hive, Hadoop Architecture, Sqoop

I am sharing here Power Point Presentation on Hadoop Architecture, Cluster, Capacity Plan.

Hadoop Components

How to user access hadoop machines

Hope that this will help us.
STEX SUPPORTS SPARK AIRDROP PROGRAM FOR XRP HOLDERS
Tags: Cryptocurrency, Press Release, Airdrop, Spark, Stex

No action needed by (XRP) Holders to claim tokens of the Flare Network

7 December 2020 — The global cryptocurrency exchange STEX announced today it will support the Flare Network token (Spark) airdrop program for Ripple (XRP) coin holders. The airdrop program will automatically delivery Spark tokens to all STEX Ripple (XRP) holders; no additional steps are necessary to claim the tokens at the Flare Network.

The Flare Network announced that a balance snapshot will be taken of all Ripple (XRP) holders on December 12, 2020. Spark tokens will then be apportioned to each remaining address according to their proportional XRP balance in the snapshot. Approximately 45 Billion Spark tokens are expected to be awarded to XRP holders when the Flare Network launches in 2021. The official date of the airdrop has not yet been announced.

STEX will subject the Flare Network (Spark) to its listing process and will determine at a future date if the token will be listed on its exchange.

For more information, visit STEX.com

About STEX

With more than 300,000 users worldwide, STEX is a powerful cryptocurrency exchange that makes it easy to buy, sell and invest digital currencies from one fast, and convenient platform at low competitive rates. Users can choose from more than 400+ trading pairs, purchase crypto with credit or bank cards, and exchange fiat to crypto or crypto to fiat with a few swipes on their mobile device. Founded in Estonia, STEX meets all EU regulations for cryptocurrency exchanges. For more information about STEX and its partners, visit:

Website: https://www.STEX.com

Twitter: https://twitter.com/StexExchangeR

Facebook: https://www.facebook.com/stex.exchanger/

Instagram: https://www.instagram.com/stex.crypto/

Telegram: https://t.me/stexcomtg

About Flare Network

Flare is the world’s first Turing Complete Federated Byzantine Agreement (FBA) network which enables customers to run smart contract services on XRP using a virtual machine powered by Ethereum.

Website: www.flare.xyz

Twitter: https://twitter.com/FlareNetworks

Telegram: https://t.me/FlareNetwork

Supporting exchanges: https://flare.xyz/supporting-exchanges/