Learn what Big Data is by exploring the 3 V's—Volume, Velocity, and Variety—and discover how Hadoop provides scalable, cost-effective solutions for storing and processing massive data sets.

apache-hadoop-and-big-data

Learn how to create and understand Hive tables, including table structure, storage formats, SerDe functions, HDFS data location, and metadata management in the Hive metastore.

behind-a-hive-table

Learn how Apache Hive buckets solve issues with dynamic partitions by reducing tiny files, enabling efficient sampling, and improving query performance through bucketing and partitioning techniques.

buckets-on-apache-hive

Learn how to set up and use Google Dataproc for fast, scalable Apache Spark and Hadoop data processing on Google Cloud, including cluster creation, job submission, and pricing details.

google-dataproc

Learn why Hadoop Distributed File System (HDFS) is essential for big data, enabling block-based storage, fault tolerance through replication, and scalable distributed file management across clusters.

hadoop-distributed-file-system-hdfs

Learn how to use Hive window and analytical functions to calculate complex metrics like a 10-day moving average on stock data with easy-to-understand SQL examples and syntax.

hive-window-and-analytic-functions

Learn how Apache Hive simplifies big data analysis by enabling SQL queries on Hadoop datasets using tables, making MapReduce easier for developers and data analysts.

introduction-to-apache-hive

Apache Pig simplifies Hadoop data processing by converting easy-to-write Pig Latin scripts into MapReduce jobs, enabling non-programmers to handle big data without coding in Java or Python.

introduction-to-apache-pig

Learn the key differences between Hive managed tables and external tables, including when to use each type and how dropping tables affects their underlying data in HDFS.

managed-table-vs-external-table

Learn how MapReduce processes large stock data to find maximum closing prices using map, shuffle, reduce phases, input splits, and optional combiners for efficient distributed computing and performance.

mapreduce-components

We are the head of census bureau for the state of California and tasked with finding the finding the population of all cities in California. All the resources we want are ready but we have only four months to finish the task. Calculating the population of all cities for a big state like california is not an easy task. The sensible thing to do is to divide the state by city and make individuals in charge of each city to calculate the population of each city where he is in charge of...

MapReduce

Learn how to optimize Hive queries by using ORDER BY, SORT BY, DISTRIBUTE BY, and CLUSTER BY to efficiently order large datasets with multiple reducers and improve performance.

order-by-vs-sort-by-vs-distribute-by-vs-cluster-by

Learn how Hive partitions and dynamic partitions optimize query performance by targeting specific data subsets, reducing scan times, and efficiently managing large datasets for faster Hive queries.

#hadoop

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