Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.

Explore hive usage efficiently in this hadoop hive project using various file formats such as JSON, CSV, ORC, AVRO and compare their relative performances

Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.

In this big data project, we will continue from a previous hive project "Data engineering on Yelp Datasets using Hadoop tools" and do the entire data processing using spark.

Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.

In this big data project, we will talk about Apache Zeppelin. We will write code, write notes, build charts and share all in one single data analytics environment using Hive, Spark and Pig.

In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.

In this big data project, we will look at how to mine and make sense of connections in a simple way by building a Spark GraphX Algorithm and a Network Crawler.

In this big data project, we will be performing an OLAP cube design using AdventureWorks database. The deliverable for this session will be to design a cube, build and implement it using Kylin, query the cube and even connect familiar tools (like Excel) with our new cube.

In this big data project, we will discover songs for those artists that are associated with the different cultures across the globe.

Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.

In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.

In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.

In this spark streaming project, we are going to build the backend of a IT job ad website by streaming data from twitter for analysis in spark.

In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.

In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.

Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.

In this Apache Spark SQL project, we will go through provisioning data for retrieval using Spark SQL.

In this spark streaming project, we are going to build the backend of a IT job ad website by streaming data from twitter for analysis in spark.

Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.

In this Spark project, we are going to bring processing to the speed layer of the lambda architecture which opens up capabilities to monitor application real time performance, measure real time comfort with applications and real time alert in case of security

In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.

The goal of this Spark project is to analyze business reviews from Yelp dataset and ingest the final output of data processing in Elastic Search.Also, use the visualisation tool in the ELK stack to visualize various kinds of ad-hoc reports from the data.

In this hive project , we will build a Hive data warehouse from a raw dataset stored in HDFS and present the data in a relational structure so that querying the data will be natural.

In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.

This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.

The goal of this spark project is to analyse the level and strength of interactions across areas of coverage of a telecom provider between different areas in the city of Milan.

In this Neo4j project, you will do network analysis using a graph database to find patterns on how a social network affects business reviews and ratings.

In this big data project, we will look at how to mine and make sense of connections in a simple way by building a Spark GraphX Algorithm and a Network Crawler.

In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.

In this Spark project, we are going to bring processing to the speed layer of the lambda architecture which opens up capabilities to monitor application real time performance, measure real time comfort with applications and real time alert in case of security

The goal of this apache kafka project is to process log entries from applications in real-time using Kafka for the streaming architecture in a microservice sense.

In this big data spark project, we will do Twitter sentiment analysis using spark streaming on the incoming streaming data.

In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.

In this spark project, we will measure by how much NFP has triggered moves in past markets.

In this project, we will look at Cassandra and how it is suited for especially in a hadoop environment, how to integrate it with spark, installation in our lab environment.

In this project, we will use complex scenarios to make Spark developers better to deal with the issues that come in the real world.

PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

In this project, we are going to talk about insurance forecast by using regression techniques.

This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.

In this big data project, we'll work with Apache Airflow and write scheduled workflow, which will download data from Wikipedia archives, upload to S3, process them in HIVE and finally analyze on Zeppelin Notebooks.

In this big data project, we will talk about Apache Zeppelin. We will write code, write notes, build charts and share all in one single data analytics environment using Hive, Spark and Pig.

In this spark streaming project, we are going to build the backend of a IT job ad website by streaming data from twitter for analysis in spark.

In this big data project, we will see how data ingestion and loading is done with Kafka connect APIs while transformation will be done with Kafka Streaming API.

In this project, we will show how to build an ETL pipeline on streaming datasets using Kafka.

In this big data project using Neo4j, we will be remodelling the movielens dataset in a graph structure and using that structures to answer questions in different ways.

In this Neo4j project, you will do network analysis using a graph database to find patterns on how a social network affects business reviews and ratings.

Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.

In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.

In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.