>
✔ 100% Money-Back Guarantee on Eligible Items
✔ Prices Displayed in Your Local Currency
✔ Final Price = No Surprise Import Fees
✔ Complimentary Insured Worldwide Shipping on Qualifying Orders
✔ Select Collector & Specialty Pieces May Require Secured Delivery Handling
by Mahmoud Parsian (Author)
Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark.
In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script.
With this book, you will:
- Learn how to select Spark transformations for optimized solutions
- Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions()
- Understand data partitioning for optimized queries
- Build and apply a model using PySpark design patterns
- Apply motif-finding algorithms to graph data
- Analyze graph data by using the GraphFrames API
- Apply PySpark algorithms to clinical and genomics data
- Learn how to use and apply feature engineering in ML algorithms
- Understand and use practical and pragmatic data design patterns
Author Biography
Mahmoud Parsian, Ph.D. in Computer Science, is a practicing software professional with 30 years of experience as a developer, designer, architect, and author. For the past 15 years, he has been involved in Java server-side, databases, MapReduce, Spark, PySpark, and distributed computing. Dr. Parsian currently leads Illumina's Big Data team, which is focused on large-scale genome analytics and distributed computing by using Spark and PySpark. He leads and develops scalable regression algorithms; DNA sequencing pipelines using Java, MapReduce, PySpark, Spark, and open source tools. He is the author of the following books: Data Algorithms (O'Reilly, 2015), PySpark Algorithms (Amazon.com, 2019), JDBC Recipes (Apress, 2005), JDBC Metadata Recipes (Apress, 2006). Also, Dr. Parsian is an Adjunct Professor at Santa Clara University, teaching Big Data Modeling and Analytics and Machine Learning to MSIS program utilizing Spark, PySpark, Python, and scikit-learn.
- In stock, ready to ship
- ✔ Authenticity Guaranteed — Verified Designer Goods
- ✔ Sourced from Authorized European/U.S. Luxury Distributors
- ✔ Secure Checkout — SSL Encrypted Payments
- ✔ Fast Global Delivery — 3–11 Business Days
- ✔ Easy Returns on Eligible Items
- ✔ 100% Money-Back Guarantee — Full Refund if Not Satisfied
AUTHENTICITY GUARANTEED
Reserved for you — complete your purchase to secure this piece.
OFFICIALLY AUTHORIZED RESELLER
Discover Officially Authorized Authentic Items at STORE7994.com - Certificates Available on Request!
Independently verified for store quality and customer safety.
Trust score: 91/100
All designer items offered by STORE 7994 are sourced from trusted luxury distributors and verified through independent authentication services.
Learn how STORE 7994 authenticates luxury items
Guaranteed Authentic — Includes Brand Documentation & Third-Party Verification Options.
Shipping information
- Free Shipping* on all orders over $300 USD to most countries* Estimated delivery: 2-5 business days Mon-Sat to U.S., CA, EU etc.
- Tracking available: DHL Express
- Store 7994 Shipping policy
- Global delivery in 3–9 business days (location dependent).
- Free Worldwide Shipping $300+. International duties & VAT are calculated by destination country and may be collected upon delivery. UK orders are subject to 20% import VAT upon delivery.
Our innovation isn’t just in the brands we carry — it’s in the way we connect them. From our automation engine that keeps collections globally updated to our commitment to authenticity-first presentation, STORE 7994 exists where timeless design meets modern precision.
Every product we offer is:
Elevated · Intentional · Exclusive · Authentic
STORE 7994 is an authorized reseller of luxury fashion houses. Certificates and proof of authenticity are available to brand owners and partners upon request.
Returns & Refunds
We want you to shop with confidence at STORE 7994. If your purchase does not meet expectations, eligible items may be returned under the conditions below.
Return Eligibility
Items must be unused, unworn, and in original condition with all tags, packaging, and accessories included. Items showing any signs of wear or damage will not be accepted.
Return Window
Return requests must be made within 14 days of delivery.
Return Shipping
Customers are responsible for return shipping costs unless the item is defective, damaged, or incorrect.
Luxury Items
Items valued over $1,000 may be subject to a 7% restocking fee upon approved return.
Non-Returnable Items
For hygiene and product integrity reasons, the following items are final sale once opened or used:
• Underwear
• Fragrances
• Any worn or used items
Made-to-Order Items
Custom-designed products, including STORE 7994 hoodies, are made exclusively for each customer and are final sale. These items are not eligible for return or exchange unless defective or incorrect.
If you receive a defective or incorrect item, please contact us and we will make it right.
International Shipping & Duties
Many of our products ship directly from trusted international partners. Any applicable customs duties or import taxes are calculated at checkout and are non-refundable, even if the item is returned.
Returns & Associated Fees
All approved returns are subject to a $24 return processing fee. For international orders, duties, taxes, and return fees will be deducted from the original payment.
Shipping Policy
Complimentary shipping is offered on orders over $300. Orders below this threshold are subject to standard shipping rates at checkout.
