Wide-column stores, also known as column-family databases, are a type of NoSQL database designed to store, retrieve, and manage large amounts of data across many commodity servers. They provide a high level of scalability and performance, which is not easily achieved in traditional relational databases.

Key Features of Wide-Column Stores

1. Scalability: Wide-column stores are horizontally scalable, meaning they handle more traffic simply by adding more servers to the database. They are designed to be distributed and can efficiently handle massive amounts of data.

2. Flexible Schema: Unlike relational databases, wide-column stores have a flexible schema. This means each row doesn't need to have the same columns, allowing for a wide variety of data types and structures to be stored.

3. Compression: Wide-column stores use compression more efficiently. Since a column often contains similar data, it's easier to compress, leading to significant space savings.

4. Performance: These databases are optimized for queries over large datasets and can fetch data more efficiently than traditional relational databases, especially for read-heavy applications.

Use Cases for Wide-Column Stores

1. Time-Series Data: Wide-column stores are excellent for storing time-series data like logs, events, or metering data. The database can easily append new data as a new column or a new column family.

2. Analytics: Wide-column stores are designed for processing large datasets, making them a good choice for analytical applications and big data processing.

3. Content Management Systems: The flexible schema of wide-column stores is beneficial for content management systems, as different types of content can have different attributes.

4. Real-time Data Analysis: These databases excel at performing real-time counting, filtering, and aggregation—typical tasks in real-time data analysis.

Examples of Wide-Column Stores

1. Apache Cassandra: A highly scalable, high-performance distributed database designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.

2. Google Bigtable: A distributed storage system for managing structured data, designed to scale to a very large size—petabytes of data across thousands of commodity servers.

3. ScyllaDB: An open-source distributed NoSQL data store, implemented in C++, it was designed to be compatible with Apache Cassandra while achieving higher throughput and lower latencies.

4. HBase: A distributed, scalable, big data store, modeled after Google's Bigtable, it provides real-time read/write access to large datasets on top of the Hadoop file system.

Python Code Snippet using Cassandra

The DataStax Python Driver for Apache Cassandra allows python applications to connect to a Cassandra cluster for data manipulation. Here is a simple example:

from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider

auth_provider = PlainTextAuthProvider(username='cassandra', password='cassandra')
cluster = Cluster(["127.0.0.1"], auth_provider=auth_provider)
session = cluster.connect()

session.execute("CREATE KEYSPACE IF NOT EXISTS test WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '1'}")
session.set_keyspace('test')

session.execute("""
    CREATE TABLE IF NOT EXISTS users (
        name text,
        age int,
        email text,
        PRIMARY KEY (name)
    )
""")

session.execute("""
    INSERT INTO users (name, age, email) 
    VALUES (%s, %s, %s)
""", ("John", 28, "john@example.com"))

rows = session.execute('SELECT name, age, email FROM users')
for row in rows:
    print(row.name, row.age, row.email)

Let's continue with our users table and create a user_activities table. The user_activities table will have name as its partition key and timeuuid as its clustering column.

from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
from cassandra import ConsistencyLevel
from cassandra.query import SimpleStatement

auth_provider = PlainTextAuthProvider(username='cassandra', password='cassandra')
cluster = Cluster(["127.0.0.1"], auth_provider=auth_provider)
session = cluster.connect()

# Create keyspace
session.execute("CREATE KEYSPACE IF NOT EXISTS test WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '1'}")
session.set_keyspace('test')

# Create users table
session.execute("""
    CREATE TABLE IF NOT EXISTS users (
        name text,
        age int,
        email text,
        PRIMARY KEY (name)
    )
""")

# Create user_activities table
session.execute("""
    CREATE TABLE IF NOT EXISTS user_activities (
        name text,
        activity_time timeuuid,
        activity text,
        PRIMARY KEY (name, activity_time)
    ) WITH CLUSTERING ORDER BY (activity_time DESC)
""")

# INSERT (Create)
session.execute("""
    INSERT INTO users (name, age, email) 
    VALUES (%s, %s, %s)
""", ("John", 28, "john@example.com"))

# For activity table, we use a SimpleStatement to specify a higher consistency level
query = SimpleStatement("""
    INSERT INTO user_activities (name, activity_time, activity)
    VALUES (%s, now(), %s)
""", consistency_level=ConsistencyLevel.LOCAL_QUORUM)

session.execute(query, ("John", "Logged in"))

# SELECT (Read)
rows = session.execute('SELECT name, age, email FROM users WHERE name=%s', ("John",))
for row in rows:
    print(row.name, row.age, row.email)

# For activity table, fetch the latest activity of John
rows = session.execute('SELECT name, activity_time, activity FROM user_activities WHERE name=%s LIMIT 1', ("John",))
for row in rows:
    print(row.name, row.activity_time, row.activity)

# UPDATE
session.execute("""
    UPDATE users 
    SET email = %s 
    WHERE name = %s
""", ("john_updated@example.com", "John"))

# DELETE
session.execute("""
    DELETE FROM users 
    WHERE name = %s
""", ("John",))

In the above code, we first create a users table and a user_activities table. We then perform the CRUD operations. Please note, when reading from the user_activities table, we are essentially performing an equivalent of a "join" operation in Cassandra. Instead of joining two tables, we query the users table to get user information and the user_activities table to get the user's latest activity.

In conclusion, wide-column stores offer scalability and flexibility for handling vast amounts of data, making them a popular choice for big data and real-time applications. They complement a robust data strategy that can adapt to the ever-changing nature and increasing volume of today's data.