Geeky Codes | AI Engineering, Data Engineering & Machine Learning Tutorials
Learn how Spark stores intermediate DataFrames and RDDs in memory, disk, or both—and when persistence can dramatically improve performance. If you work with Apache Spark long enough, you’ll eventually encounter a situation like this: You have a DataFrame that takes several minutes to compute, and you use it multiple times in your pipeline. Without persistence, … Read more
Spark operations like groupBy(), joins, and distinct() can lead to expensive data shuffling in large datasets, which may not be evident during development. The cost arises from the process of redistributing data among executors, often causing prolonged runtime. Careful optimization and understanding of shuffles are essential to enhance performance and reduce costs effectively.
This tutorial emphasizes the significance of partitioning in Apache Spark, detailing how it optimizes parallel processing, resource utilization, and performance. It covers partition creation, management functions like repartition() and coalesce(), and the impact of data skew. Practical strategies for effective partitioning strategies are also discussed to enhance performance and minimize costs.
If you’ve worked with Apache Spark for even a short time, you’ve probably encountered one of the most important—and potentially expensive—operations in distributed data processing: Shuffle. Whenever Spark needs to move data between partitions, a shuffle may occur. This commonly happens during operations such as: Understanding shuffle is critical if you’re working with PySpark, Databricks, … Read more
Apache Spark utilizes accumulators to enable executors to communicate aggregated values back to the driver while processing massive datasets. These shared variables are ideal for counting or tracking diagnostic information. While they are useful for monitoring, accumulators should not guide critical business logic due to potential issues with task retries and lazy evaluation, making DataFrame and SQL operations more reliable for business analytics.
This tutorial discusses Broadcast Variables in Apache Spark, highlighting their role in optimizing data distribution across executors. By broadcasting small, read-only data instead of transferring it with every task, Spark reduces network overhead. The guide includes practical examples, memory considerations, and distinctions between broadcast variables and broadcast joins, emphasizing efficient data handling in large datasets.
How User-Defined Functions work inside Spark—and why experienced PySpark engineers treat them carefully You have a DataFrame. You need to apply a custom transformation. So you write a Python function: Then you turn it into a UDF: It works. But there is an important question: Should you actually use a UDF? In PySpark, the answer … Read more
groupBy() looks simple in PySpark, but Aggregations scale and can trigger one of Spark’s most expensive operations: a shuffle. If you’re preparing for PySpark interviews or working with Spark in production, follow me for practical Data Engineering, AI/ML, and PySpark content. 📩 Subscribe by email to get new tutorials and interview questions delivered straight to … Read more
This article emphasizes an optimization mindset for Data Engineers using Databricks. It highlights the importance of understanding Apache Spark’s execution to minimize unnecessary compute costs. Key strategies include efficient data handling, optimizing code, and analyzing performance metrics. Ultimately, the goal is to make code smarter rather than solely increasing resources when addressing rising bills.
Preparing for AI Engineer interviews requires understanding that roles differ significantly across companies. Interviews may focus on AI theory, system engineering, or LLM interactions. Candidates should analyze job descriptions, prepare specific questions, and structure responses around the role’s requirements. Emphasizing reasoning over memorization enhances interview readiness and relevance.
This content focuses on the significance of window functions in Apache Spark and PySpark for solving complex analytical problems that require calculations across related rows while retaining the original data. It provides a comprehensive guide on various window functions, their specifications, real-world applications, performance considerations, and common pitfalls, ultimately emphasizing their utility in data engineering tasks.
Part 5 moves beyond Spark API knowledge into the decisions that actually matter when a PySpark pipeline is slow, unstable, expensive, or producing incorrect results. If you’re preparing for PySpark interviews or working with Spark in production, follow me for practical data engineering and AI/ML content. 📩 Subscribe by email to get new tutorials and … Read more
You can write dozens of Spark transformations without Spark processing a single record. The moment you call an action, the lazy plan becomes an actual computation. If you have already learned about Spark transformations, you have seen code like: At this point, Spark has not necessarily read and processed all the data. Now add: Something … Read more
Learn how Spark DataFrames transform data lazily, how transformations build execution plans, and which operations can trigger expensive shuffles. Apache Spark Learning Path APACHE SPARK LEARNING PATH Where Are We Now? We’ve already learned how Spark reads data and how its execution engine works. Now we need to understand the most common thing we actually … Read more
Stop using ChatGPT only to get answers. Learn how to use it to plan, execute, verify, and improve complete tasks. AI assistants are becoming much more useful than traditional question-and-answer chatbots. Instead of asking: “Explain how to build a RAG application.” you can structure the task as: “Design the architecture, implement the application, test it, … Read more
This tutorial covers how Apache Spark writes data to files, tables, and databases, detailing the use of DataFrameWriter and various formats like Parquet, CSV, and JSON. It explores save modes, partitioning techniques, and common mistakes while writing data, ultimately aiming to enhance efficiency in data processing and querying.
A practical guide to uploading, downloading, filtering, and managing files in AWS S3 with Python Amazon S3 is one of the most commonly used AWS services for storing files and datasets. In the previous tutorial, we learned how to list and print the contents of an S3 bucket using Python and Boto3. But listing files … Read more
Understand the three core Spark data abstractions, how they differ, why DataFrames became the preferred choice for most workloads, and when RDDs or Datasets still make sense. If you’ve started learning Apache Spark, you’ve probably encountered three terms repeatedly: RDD. DataFrame. Dataset. At first, they can seem interchangeable. After all, all three allow you to … Read more
Understand what SparkSession is, why modern Spark applications need it, what it actually creates, and how it differs from SparkContext If you’ve written PySpark code, you’ve probably started with something like this: And then immediately started working with DataFrames: But what exactly is spark? Why do we need a SparkSession? What happens when getOrCreate() is … Read more
Learn how Spark reads data from files and storage systems, how schema inference works, and how to build reliable data ingestion pipelines with PySpark Almost every Spark application starts with one simple question: Where is the data, and how do I get it into Spark? In PySpark, the answer usually starts with: For example: or: … Read more