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The use of Hive or the hive meta-store is so ubiquitous in big data engineering that achieving efficient use of the tool is a factor in the success of many big data projects. Whether in integrating with Spark or using hive as an ETL tool, many big data projects either fail or succeed as they grow in scale and complexity because of decisions made in the early lifecycle of the analytics project.
In this hive project, we will explore using hive efficiently and this big data project format will take an exploratory pattern rather than a project building pattern. The goal of these sessions will be to explore Hive in uncommon ways towards mastery.
We will be using different sample dataset for hive in the series of these hive real time projects, exploring different Hadoop file formats like text, CSV, JSON, ORC, parquet, AVRO and sequence file, will look at compression and different codecs and take a look at the performance of each when you try integration with either spark or impala. The idea of this hadoop hive project is to explore enough so that we can be made a reasonable argument about what to do or not in any given scenario.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
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.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.