#spark course

[poorly-drawn-mdzs]

I love you Safety Wizard.

(Inspired by @keroascrazy)

[mysticpandakid]

What is PySpark? A Beginner’s Guide 

Introduction 

The digital era gives rise to continuous expansion in data production activities. Organizations and businesses need processing systems with enhanced capabilities to process large data amounts efficiently. Large datasets receive poor scalability together with slow processing speed and limited adaptability from conventional data processing tools. PySpark functions as the data processing solution that brings transformation to operations.  

The Python Application Programming Interface called PySpark serves as the distributed computing framework of Apache Spark for fast processing of large data volumes. The platform offers a pleasant interface for users to operate analytics on big data together with real-time search and machine learning operations. Data engineering professionals along with analysts and scientists prefer PySpark because the platform combines Python's flexibility with Apache Spark's processing functions.  

The guide introduces the essential aspects of PySpark while discussing its fundamental elements as well as explaining operational guidelines and hands-on usage. The article illustrates the operation of PySpark through concrete examples and predicted outputs to help viewers understand its functionality better. 

What is PySpark? 

PySpark is an interface that allows users to work with Apache Spark using Python. Apache Spark is a distributed computing framework that processes large datasets in parallel across multiple machines, making it extremely efficient for handling big data. PySpark enables users to leverage Spark’s capabilities while using Python’s simple and intuitive syntax. 

There are several reasons why PySpark is widely used in the industry. First, it is highly scalable, meaning it can handle massive amounts of data efficiently by distributing the workload across multiple nodes in a cluster. Second, it is incredibly fast, as it performs in-memory computation, making it significantly faster than traditional Hadoop-based systems. Third, PySpark supports Python libraries such as Pandas, NumPy, and Scikit-learn, making it an excellent choice for machine learning and data analysis. Additionally, it is flexible, as it can run on Hadoop, Kubernetes, cloud platforms, or even as a standalone cluster. 

Core Components of PySpark 

PySpark consists of several core components that provide different functionalities for working with big data: 

RDD (Resilient Distributed Dataset) – The fundamental unit of PySpark that enables distributed data processing. It is fault-tolerant and can be partitioned across multiple nodes for parallel execution. 

DataFrame API – A more optimized and user-friendly way to work with structured data, similar to Pandas DataFrames. 

Spark SQL – Allows users to query structured data using SQL syntax, making data analysis more intuitive. 

Spark MLlib – A machine learning library that provides various ML algorithms for large-scale data processing.��

Spark Streaming – Enables real-time data processing from sources like Kafka, Flume, and socket streams. 

How PySpark Works 

1. Creating a Spark Session 

To interact with Spark, you need to start a Spark session. 

Output: 

2. Loading Data in PySpark 

PySpark can read data from multiple formats, such as CSV, JSON, and Parquet. 

Expected Output (Sample Data from CSV): 

3. Performing Transformations 

PySpark supports various transformations, such as filtering, grouping, and aggregating data. Here’s an example of filtering data based on a condition. 

Output: 

4. Running SQL Queries in PySpark 

PySpark provides Spark SQL, which allows you to run SQL-like queries on DataFrames. 

Output: 

5. Creating a DataFrame Manually 

You can also create a PySpark DataFrame manually using Python lists. 

Output: 

Use Cases of PySpark 

PySpark is widely used in various domains due to its scalability and speed. Some of the most common applications include: 

Big Data Analytics – Used in finance, healthcare, and e-commerce for analyzing massive datasets. 

ETL Pipelines – Cleans and processes raw data before storing it in a data warehouse. 

Machine Learning at Scale – Uses MLlib for training and deploying machine learning models on large datasets. 

Real-Time Data Processing – Used in log monitoring, fraud detection, and predictive analytics. 

Recommendation Systems – Helps platforms like Netflix and Amazon offer personalized recommendations to users. 

Advantages of PySpark 

There are several reasons why PySpark is a preferred tool for big data processing. First, it is easy to learn, as it uses Python’s simple and intuitive syntax. Second, it processes data faster due to its in-memory computation. Third, PySpark is fault-tolerant, meaning it can automatically recover from failures. Lastly, it is interoperable and can work with multiple big data platforms, cloud services, and databases. 

Getting Started with PySpark 

Installing PySpark 

You can install PySpark using pip with the following command: 

To use PySpark in a Jupyter Notebook, install Jupyter as well: 

To start PySpark in a Jupyter Notebook, create a Spark session: 

Conclusion 

PySpark is an incredibly powerful tool for handling big data analytics, machine learning, and real-time processing. It offers scalability, speed, and flexibility, making it a top choice for data engineers and data scientists. Whether you're working with structured data, large-scale machine learning models, or real-time data streams, PySpark provides an efficient solution. 

With its integration with Python libraries and support for distributed computing, PySpark is widely used in modern big data applications. If you’re looking to process massive datasets efficiently, learning PySpark is a great step forward. 

[emberglowfox]

forgot to ever post this. unfinished thing of my dorm room

[teophan]

a fantastic commission from @themetalhiro of my Dungeons & Devil Fruits 5e Sniper Marksman, Vinsmoke “Starfall Gunner” Beretta!

it was an absolute treat to work with you, looking forward to the next opportunity to do so!

[sleevebuscemii]

like i said. they dont even fall to that ground. art deliberately lowers patrick onto it.

[peace-hunter]

I need to know what the thirteen’s reaction was to first seeing Bumblebee I bet they where like he is definitely our new nephew because Optimus is basically his dad no matter the continuity

they love him dearly <33 and optimus is not above using this to his advantage <333

haunted au

[dragon-drop1477]

Read No Choice at All by Triscribe and could not get the concept out of my head

[reksink]

A Lounge of Lizards

[seldompathic]

If bro smiles through tears in episode 1 of S3 I'm gonna fold like a lawn chair

[ricksanchezbignaturals]

sorry for still not being over this second long shot after 7 months (holy shit, unmortricken was 7 months ago) but it's literally the gayest shit i've ever seen like c'mon COME ON BRO

they were going in for a punch at the same time but quite literally became stuck together because they got too close. their arms weren't even opened up or anything, their cybernetics tore through skin and sleeves to get tangled up in each other. the way this only happens because they're both ricks. they get their wires crossed because they're too similar. so similar their own bodies can't tell the difference, automatically trying to attach them like they're each other's lost limb.

i think i hauve covid

[starry-bi-sky]

MMMM twins au with danny and dan except its og TUE timeline danny and dan

ok okay i neeeeed o write this down and share it before i explode buT. as the title says. twins danny and dan (who im going to be calling James/Jamie bc i cannot express how much i despise the name dan) where, instead of disappearing into the ghost zone after he's separated from danny's body, Dan rips out Vlad's ghost half, tears THAT in half, and fuses one half with himself and the other with Danny.

Shit happens, and BOOM. Two morally ambiguous and perhaps slightly murderous demonic twins from hell. Daniel James Fenton and his Twin WHose Always Been Here What Are You Talking About :) James Daniel Fenton. They are both depressed, lonely, and one bad day from becoming a mass extinction event :)

this is because i got grabbed by the hair today and dragged into the SVSS fandom screaming and the fanart of Shen Jiu/Shen Yuan/Shen Quingqiu (????) with his fan entranced me. Ice Prince Core is my favorite thing so naturally i have to implant that onto my favorite blorbos ever :)

After the Incident, both their appearances changed and they're practically identical to each other. Sorta. They both have heterochromia and salt-and-pepper hair. But Danny has one green eye and one blue eye and white hair with black streaks, while Jamie has one blue eye and one green eye and black hair with white streaks. I'm iving them both long hair, for funsies <3

nobody can tell them apart, they keep getting confused on whose who and frankly the mix-match hair and eyes make it worse not better asjd. they're horrifically codependent. please do not separate :)

and because i must. im pulling a blood blossom/tales of the passerine and giving them to pre-robin batman. batman and his terrifying demon(??) twins. nobody is quite sure if they're human or not, and the scourge of gotham are a little too terrified to ask.

(they dont HAVE to go to batman while he's pre-robin. however. i think its much funnier that way bc gotham isn't use to A) Batman having kids, and B) Batman having TERRIFYING kids yet. think of all the new fun rumors)

they both use war fans while they're out, and neither of them use their ghost forms because they at least have the remaining empathy to know that they're more likely to murder someone accidentally as a ghost :). Ghost form is for fellow mythicals and Functionally Immortals Only! Not for Squishy Humans.

Jamie: murder. bloodshed. revengggee Bruce: no. no. Justice. peace!! hope! Danny: bittinngggg. blooood. ^-^

They're honestly not bad kids they're just horrifically traumatized two halves of a whole that can never be reunited ever again :).

idk what their vigilante names are but i do know that the underground refer to them in horrified whispers as 'the twins'. this all stemmed from the desperate and sudden urge to see Danny and Jamie, as their vigilante selves, hiding the lower half of their faces with fans and looking terrifyingly judgmental while they do it <333

[arthursfuckinghat]

You know, whenever I see the discussions around Jack Marston possibly getting drafted in world war one, I can't help but physically ache at the thought of it.

Jack Marston, born into a gang that honoured freedom above everything, forced to sacrifice everything he has left for war.

Jack Marston, a boy who read about knights and soldiers, now forced to become one in another fight he never asked for.

Jack Marston, raised to be away from a life of violence, but now the world has found a way to drag him back in.

No matter what happens, Jack would have to face a really tragic dilemma. Does he go to war and sacrifice the legacy of freedom he was raised with? The life his family died for? Or refuse and be labeled a criminal, putting his parents to shame and repeating the same cycle his father went through?

It just tragically mirrors the struggle he’s always had - trying to find his own identity outside the legacy of John Marston, and the violence that came with it. But he's being pulled back in, no matter what he chooses.

He was never made for the violence that shaped his parents' life.

[cupiidzbow]

I was talking abt food dynamics in my ships on twitter and i was just tryna think of them at the top of my head for the both of us

[i-dreamed-i-had-a-son]

There is a brutal contrast in how Megatron and Optimus's stories end. Optimus nobly surrenders his spark, along with the Matrix of Leadership, having been sent off by his dear and trusted friends after giving them a fond farewell. He has accomplished his life's greatest goal; now he is able to rest. He dies willingly, at peace, bringing hope and giving life to the next generation by restoring the Allspark.

Megatron had already been killed, and was told that he can never join that very Allspark. Having been enslaved, and only released because of Optimus's actions, he is now reanimated, inhabiting his own corpse. His only remaining ally, who has constantly betrayed him, does not understand him. He flies off, alone, having lost all his forces and failed in his mission, and realizing that his life's greatest purpose was misguided. He is left, we can only assume, to live a hellish and hopeless life--a shell, consumed by the past.

And he did not tell Optimus goodbye. He knows that Optimus will leave him behind, will obtain the heaven he can never enter. He gives him the parting gift of his repentance, to acknowledge this. But in his refusal to say farewell, there is an echo of his own wretched endurance. He does not get to rest. Megatron will remain, eternally alone--eternally waiting for a reunion that will never come.

[1million]

ZHEANI — Union Stage, DC

photos by me

[theoneinmultiplefandoms]

did ya'll know that Doctor Who's anniversary takes place on the same day as my bday?? that's crazyyyyyy 🏃‍♀️🏃‍♂️

bonus cringe: sketches i made from around 2017, proving that i had this idea on me for a longggg time FHSJDFH /lh