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Python Set Operations Explained: From Theory to Real-Time Applications

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A  set  in Python is an unordered collection of unique elements. It is useful when storing distinct values and performing operations like union, intersection, or difference. Real-Time Example: Removing Duplicate Customer Emails in a Marketing Campaign Imagine you are working on an email marketing campaign for your company. You have a list of customer emails, but some are duplicated. Using a set , you can remove duplicates efficiently before sending emails. Code Example: # List of customer emails (some duplicates) customer_emails = [ "alice@example.com" , "bob@example.com" , "charlie@example.com" , "alice@example.com" , "david@example.com" , "bob@example.com" ] # Convert list to a set to remove duplicates unique_emails = set (customer_emails) # Convert back to a list (if needed) unique_email_list = list (unique_emails) # Print the unique emails print ( "Unique customer emails:" , unique_email_list) Ou...
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Big Data: IBM InfoSphere BigInsights Basics

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I am explaining here why you need IBM infoSphere. You all know about what is file system in Hadoop.
Hadoop is a distributed file system and data processing engine that is designed to handle extremely high volumes of data in any structure.
In simpler terms, just imagine that you've got dozens, or even hundreds (or thousands!) of individual computers racked and networked together. Each computer (often referred to as a node in Hadoop-speak) has its own processors and a dozen or so 2TB or 3TB hard disk drives.

All of these nodes are running software that unifies them into a single cluster, where, instead of seeing the individual computers, you see an extremely large volume where you can store your data.

The beauty of this Hadoop system is that you can store anything in this space: millions of digital image scans of mortgage contracts, days and weeks of security camera footage, trillions of sensor-generated log records, or all of the operator transcription notes from a call center. 

This ingestion of data, without worrying about the data model, is actually a key tenet of the NoSQL movement.

IBM InfoSphere BigInsights


BigInsights features Apache Hadoop and its related open source projects as a core component. This is informally known as the IBM Distribution for Hadoop. IBM remains committed to the integrity of these open source projects and will ensure 100 percent compatibility with them.
BigInsights is IBM Open Source for Hadoop
This fidelity to open source provides a number of benefits. For people who have developed code against other 100 percent open source–compatible distributions, their applications will also run on BigInsights, and vice versa. This open source compatibility has enabled IBM to amass over 100 partners, including dozens of software vendors, for BigInsights.

Simply put, if the software vendor uses the libraries and interfaces for open source Hadoop, they'll work with BigInsights as well.

Components in IBM Infosphere Biginsights

Hadoop (common utilities, HDFS, and the MapReduce framework)

1.0.3

Avro (data serialization)

1.6.3

Chukwa (monitoring large clustered systems)

0.5.0

Flume (data collection and aggregation)

0.9.4

HBase (real-time read and write database)

0.94.0

HCatalog (table and storage management)

0.4.0

Hive (data summarization and querying)

0.9.0

Lucene (text search)

3.3.0

Oozie (work flow and job orchestration)

3.2.0

Pig (programming and query language)

0.10.1

Sqoop (data transfer between Hadoop and databases)

1.4.1

ZooKeeper (process coordination)

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