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Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More
This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
Spark 2 offers a huge but yet backward-compatible break from the Spark 1.x, not only in terms of high-level API but also in performance. And spark the module with the most significant new features is Spark SQL.
In this apache spark project, we will explore a number of this features in practice.
We will discuss using various dataset, the new unified spark API as well as the optimization features that makes Spark SQL the first way to explore in processing structured data.
However, there are times when it is inevitable to resort to Spark Core - RDD in Spark 2. We will explore that as well alongside the newest and cool structured streaming API that enables fault-tolerant stream processing engine built on the Spark SQL engine.
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 project, we will measure by how much NFP has triggered moves in past markets.
In this project, we will look at two database platforms - MongoDB and Cassandra and look at the philosophical difference in how these databases work and perform analytical queries.