for Data Enthusiasts
General Information
By the end of this read, you’ll understand why Apache Iceberg is not just another open table format — it’s the seismic shift that’s redefining the role of Delta Lake in the modern data ecosystem and transform how the world thinks about data lakes. From Data Lakes to Data Icebergs: A New Era Begins Over … Read more
The Era of Chaos – and Snowflake’s Rise Back in 2017, most of us were drowning in messy data. Files were everywhere in S3 buckets, Hadoop jobs kept failing at the worst times, and analysts? They were always chasing clean data that never seemed to arrive when needed. It was frustrating, and honestly, it felt … Read more
Tired of messy Python setups? Ever screamed at your terminal? Been there, done that, deleted Anaconda. Let me show you how I set up a clean, beautiful, and powerful Python development environment on my Mac. It’s light, customizable, and perfect for devs who love a good-looking terminal and tight control over Python versions. 🤓 Why … Read more
Python offers a rich set of built-in and extended data structures to efficiently manage and process data. In this blog, we’ll deep dive into essential ones: List, Tuple, Dictionary (Dict), Set, Frozenset, and also explore some powerful structures from the collections and dataclasses modules. We’ll cover their properties, use-cases, constructors, and how to convert between them using intuitive examples. Note: Since Python 3.7, dictionaries … Read more
When writing SQL queries, it is essential to understand the order in which SQL clauses are executed. This helps in writing optimized queries, especially when transitioning from SQL to PySpark. In this blog, we’ll walk you through the SQL execution order, the SQL clauses, and provide their corresponding PySpark syntax. SQL Execution Order and Corresponding … Read more
Apache Spark has revolutionized the way we process large-scale data — delivering unparalleled speed, scalability, and flexibility. But as many engineers discover, achieving optimal performance in Spark is far from automatic. Your job runs — but takes longer than expected. The cluster scales — but the costs rise disproportionately. Memory errors appear out of nowhere. … Read more
Background File Formats Evolution Why not use CSV/XML/JSON? Repeated or no meta information. Files are not splittable, so cannot be used in a map-reduce environment. Missing/ Limited schema definition and evolution support. Can leverage “JsonSchema” to maintain schema separately for JSON. It may still require transformation based on a schema, so why not consider Avro/Proto? … Read more
Observations : Count(1) or Count(*) – This is never expanded on each column individually so will work perfectly fine on complete data. Count(1) is more optimized then Count(*) Count(source.*) – source represents “Left table” of “Left Outer Join”: This will be evaluated as Count(source.col1, source.col2, …. source.colN ) So, if any column has NULL, then the complete row … Read more
I was doing some cost comparison of using CSV files vs Parquet File. Interestingly, when using Parquet format, data scanning for similar queries, cost 99% less as compared to CSV format. Queries ( Mentioned only for Parquet) CSV ( 11.32 GB )Run Time (in sec) CSV ( 11.32 GB )DataScanned (in GB) PARQUET ( 4.1 GB )Run Time (in sec) PARQUET ( 4.1 GB )DataScanned (in GB) … Read more
This post was originally posted in my first blog — learntheprogramming.blogspot.com A “for” loop allows code to be repeatedly executed and is classified as an iteration statement.Unlike many other kinds of loops, such as the while loop, the for loop is often distinguished by an explicit loop counter or loop variable. This allows the body … Read more