Featured Post

Step-by-Step Guide to Reading Different Files in Python

-
Image
 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
Post a Comment

SAN: Real Architecture Explained

-
A SAN is connected behind the servers. SANs provide block-level access to shared data storage. Block level access refers to the specific blocks of data on a storage device as opposed to file level access. One file will contain several blocks.
The simplified SAN architecture to understand how data is stored in storage from servers





Storage Area Networks (SANs)
  • SANs provide high availability and robust business continuity for critical data environments. SANs are typically switched fabric architectures using Fibre Channel (FC) for connectivity.
  • The term switched fabric refers to each storage unit being connected to each server via multiple SAN switches also called SAN directors which provide redundancy within the paths to the storage units. This provides additional paths for communications and eliminates one central switch as a single point of failure.
  • Ethernet has many advantages similar to Fibre Channel for supporting SANs. Some of these include high speed, support of a switched fabric topology, widespread interoperability, and a large set of management tools.
  • In a storage network application, the switch is the key element. With the significant number of Gigabit and 10 Gigabit Ethernet ports shipped, leveraging IP and Ethernet for storage is a natural progression for some environments. 
SAN Vs IP
  1. IP was developed as an open standard with complete interoperability of components. Two new IP storage network technologies are Fibre Channel over Ethernet (FCoE) and SCSI over IP (iSCSI). IP communication across a standard IP network via Fibre Channel Tunneling or storage tunneling has the benefit of utilizing storage in locations that may exceed the directly attached limit of nearly 10 km when using fiber as the transport medium.
  2. Internal to the data center, legacy Fibre Channel can also be run over coaxial cable or twisted pair cabling, but at significantly shorter distances.
  3. The incorporation of the IP standard into these storage systems offers performance benefits through speed, greater availability, fault tolerance, and scalability. These solutions, properly implemented, can almost guaranty 100% availability of data. The IP based management protocols also provide network managers with a new set of tools, warnings, and triggers that were proprietary in previous generations of storage technology. Security and encryption solutions are also greatly enhanced. With 10G gaining popularity and the availability of new faster WAN links, these solutions can offer true storage on demand.

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

SQL Query: 3 Methods for Calculating Cumulative SUM

-
Image
SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than...
Read more

5 SQL Queries That Popularly Used in Data Analysis

-
Image
 Here are five popular SQL queries frequently used in data analysis. 1. SELECT with Aggregations Summarize data by calculating aggregates like counts, sums, averages, etc. SELECT department, COUNT(*) as employee_count, AVG(salary) as average_salary FROM employees GROUP BY department; 2. JOIN Operations  Combine data from multiple tables based on a related column. SELECT e.employee_id, e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.department_id; 3. WHERE Clause for Filtering Filter records based on specified conditions. SELECT * FROM sales WHERE sale_date BETWEEN '2024-01-01' AND '2024-12-31'   AND amount > 1000; 4. ORDER BY Clause for Sorting Sort results in ascending or descending order based on one or more columns. SELECT product_name, price FROM products ORDER BY price DESC; 5. GROUP BY with HAVING Clause Group records and apply conditions to the aggregated results. SELECT department, SUM(salary) as total_salaries FROM employ...
Read more

Step-by-Step Guide to Reading Different Files in Python

-
Image
 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
Read more