Working with Big Data in Python
Where to Watch Working with Big Data in Python
21
Predicting Up Votes Using pyspark.ml
2018-02-20
The popularity of Reddit comments depends on lots of aspects, can a predictive model help understand how to have popular posts? Let us explore and find an answer to this in this video.
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20
Preparing Data for Prediction Task Using spark.ml
2018-02-20
Spark.ml requires dataframe columns to be of type Vector. Spark dataframe columns can store data of many different types.
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19
Loading Data from MongoDB in Spark, Transform into Pandas DF
2018-02-20
Data stored in MongoDB needs to be made available to Spark in a data structure that is valid.
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18
Making Reddit Data Available to PySpark
2018-02-20
JSON data often exists in data dumps rather than being extracted from an API incrementally. Reddit is a popular site for posting and commenting.
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17
Connecting to MongoDB with PySpark
2018-02-20
If our data resides in MongoDB, we need to extract it into a Spark data structure to analyze it.
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15
Data Structures in Spark
2018-02-20
Spark data structures are key to building effective processing pipelines; understand the difference between RDDs and dataframes.
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14
What Is Spark and When Do We Need It?
2018-02-20
Modern datasets are challenging to process as our memory and processing needs are large and variable. Spark helps to scale analysis over a cluster of processors.
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13
Querying Weather Data from MongoDB
2018-02-20
Raw data doesn't provide insights; develop pipeline aggregation operations to summarise and filter data in an iterative fashion.
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11
Grabbing Weather Data via OpenWeather API
2018-02-20
Web APIs are a common source of data, learn how to leverage pyMongo and requests to extract useful information from API data.
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10
Using Operators, Updates, and Aggregations
2018-02-20
Using what you have learnt about finding and matching documents also learn how to use operators and the aggregate function to calculate aggregated statistics and update documents.
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9
Return Codes and Exceptions
2018-02-20
MongoDB is a highly scalable database capable of many simultaneous connections. Sometimes this causes errors in operations.
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8
Inserting and Finding Documents
2018-02-20
MongoDB provides a rich syntax to precisely control which data is returned from a query using the query and projection operators. Learn how to specify these arguments.
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7
Using pyMongo Cursors
2018-02-20
Often our queries return large numbers of documents through cursors. Let us learn how cursors work, so we can deal with these queries without a large memory footprint.
Watch Working with Big Data in Python Season 1 Episode 7 Now
6
Setting Up pyMongo
2018-02-20
pyMongo is the Python API for MongoDB. Let us learn how to get up and running with pyMongo.
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5
Setting Up MongoDB and Running Our First MongoDB Query
2018-02-20
Get up and running with fundamental MongoDB operations like creating a database and storing/retrieving documents through the Mongo shell.
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4
MongoDB Indices and Datatypes
2018-02-20
MongoDB, like SQL can use indexed data to speed up common queries.
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3
From Tabular Data to JSON Documents
2018-02-20
Often, we consider data in tabular form, but JSON data is the data format of modern web applications. JavaScript Object Notation is a natural format for big data applications.
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2
What Is MongoDB and Why Should I Use It?
2018-02-20
Let us explore and discover what a non-relational database is. MongoDB is a non-relational database.
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1
The Course Overview
2018-02-20
This video provides an overview of the entire course.
Watch Working with Big Data in Python Season 1 Episode 1 Now
Working with Big Data in Python is a series categorized as a new series. Spanning 1 seasons with a total of 19 episodes, the show debuted on 2018. The series has earned a no reviews from both critics and viewers. The IMDb score stands at undefined.
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How can I watch Working with Big Data in Python online? Working with Big Data in Python is available on Packt Publishing with seasons and full episodes. You can also watch Working with Big Data in Python on demand at Amazon online.
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