Lalitha Mohanasundaram🌟 BigQuery: Effortless Table Referencing for our Queries🌟BigQuery’s strength lies in its ability to analyze large datasets. 😊 It focuses on Online Analytical Processing (OLAP). However, managing…2d ago2d ago
Lalitha Mohanasundaram🌟BigQuery 101: Serverless Data Analysis Made Easy🌟In today’s data-heavy world, organizations are gathering information at a fast pace. Dealing with and understanding these large sets of…4d ago4d ago
Lalitha Mohanasundaram🌟Streamline Spark Jobs: Count Efficiently, Share Data Smartly🌟Dealing with large datasets in Apache Spark can be challenging. This post aims to assist by delving into two useful tools: accumulators and…Jun 21Jun 21
Lalitha Mohanasundaram🌟Dealing with Small Files in Hadoop🌟Hadoop, the powerful warrior of big data processing, can easily handle terabytes of information. However, even the mightiest heroes have…Jun 10Jun 10
Lalitha Mohanasundaram🌟Leveling Data Skewness using Salting🌟Data skewness refers to the uneven distribution of data across partitions or processing units. In a skewed dataset, some partitions may…Jun 3Jun 3
Lalitha Mohanasundaram🌟Choosing the Right Compression Codec for Big Data System🌟Compression codecs act like superheroes in the big world of data, where a huge amount of information can feel too much. They help by making…May 27May 27
Lalitha Mohanasundaram🌟 Improving Spark Job Performance by Gaining a Better Understanding of DAG Execution🌟Spark optimization is essential for maximizing the efficiency and performance of our data processing workflows. At the core of Spark…May 20May 20
Lalitha Mohanasundaram🌟Maximizing Spark Performance By Understanding and Optimizing Garbage Collection🌟What is garbage collection in Spark?🤔May 14May 14
Lalitha Mohanasundaram🌟Maximizing Query Efficiency using Predicate Pushdown🌟What is Predicate pushdown?May 6May 6