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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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HBASE: Top Features in Storing Big data

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In this post explained top features added in HBase to handle the data. The Java implementation of Google's Big Table you can call it as HBASE.  In HBase, the data store as two parts.
hadoop hbase

Row Key : 00001 
Column : (Column Qualifier:Version:Value)

Features of HBASE

  • HBase data stores consist of one or more tables, which are indexed by row keys.
  • Data is stored in rows with columns, and rows can have multiple versions.
  • By default, data versioning for rows is implemented with time stamps.
  • Columns are grouped into column families, which must be defined upfront during table creation. Column families are stored together on disk, which is why HBase is referred to as a column-oriented datastore
New features of HBASE check now

In addition...

HBase is a distributed data store, which leverages a network-attached cluster of low-cost commodity servers to store and persist data.HBase architecture is a little trick to know.

Region Servers...

RegionServers are the software processes (often called daemons) you activate to store and retrieve data in HBase.

The big difference...

  • HABSE handles growing data or big data. HBase automatically scales as you add data to the system. A huge benefit compared to most database management systems, which require manual intervention to scale the overall system beyond a single server. 
  • With HBase, as long as you have in the rack another spare server that's configured, scaling is automatic.

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