Instructed by a group that incorporates 2 Stanford-taught, 2 ex-Flipkart, and ex-Googlers Lead Analysts. This group has many years of down to earth involvement with working with enormous scope information handling works.
Learning Style
Learning Style
Difficulty
Course Duration
Instructed by a group that incorporates 2 Stanford-taught, 2 ex-Flipkart, and ex-Googlers Lead Analysts. This group has many years of down to earth involvement with working with enormous scope information handling works.
Instructed by a group that incorporates 2 Stanford-taught, 2 ex-Flipkart, and ex-Googlers Lead Analysts. This group has many years of down to earth involvement with working with enormous scope information handling works.
Pig is relevantly named, will devour any information that you toss at it and make a few bucks!
Let's parse that
Omnivorous: Pig functions with unstructured information. It has numerous tasks that are very SQL-like yet Pig can play out this procedure on informational collections that have no fixed blueprint. Pig is extraordinary at wrestling information into a structure that is perfect and can be put away in an information stockroom for analysis and reporting.
Make home a few bucks: Pig permits you to change information such that makes is organized, unsurprising and helpful, prepared for utilization.
Designers who need to parse and extricate helpful data from enormous datasets
Experts who need to wrangle enormous, unstructured information into shape
Pig Basics: Complex and Scalar information types (Maps, Bags, Tuples), fundamental transformations, for example, Foreach, Filter, Dump, Store, Load, Limit, Distinct, Order by and other inherent capacities.
Advanced-Data Optimizations and Transformations: The brain-twisting Nested Foreach, Links and their enhancements utilizing "merge", "parallel", "replicated" and different Co-gatherings, keywords, and Semi-joins, troubleshooting utilizing Illustrate and Explain directions
True model: Clean up server logs utilizing Pig
Working with Pig requires some essential information on the SQL question language, a concise comprehension of the MapReduce and Hadoop eco-framework.
| Subjects | Big Data |
|---|---|
| Lab Access | No |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 6 Hours |
| Language | English |
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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