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We have come to learn that Hadoop's distributed file system was engineered to favor fewer larger files over many small files. However, we mostly would not have control over how data come. Many data ingestion to data infrastructures come in small bits and whether we are implementing a data lake on HDFS or not, we will have to deal with this data inputs.
In this online hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to resolve the small file problem in hadoop.
We will start by defining what it means, how inevitable this situation could arise, how to identify bottlenecks in a hadoop cluster owing to the small file problem and varieties of ways to solve them.
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.
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
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.
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.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.