Featured Post

How to Read a CSV File from Amazon S3 Using Python (With Headers and Rows Displayed)

-
Image
  Introduction If you’re working with cloud data, especially on AWS, chances are you’ll encounter data stored in CSV files inside an Amazon S3 bucket . Whether you're building a data pipeline or a quick analysis tool, reading data directly from S3 in Python is a fast, reliable, and scalable way to get started. In this blog post, we’ll walk through: Setting up access to S3 Reading a CSV file using Python and Boto3 Displaying headers and rows Tips to handle larger datasets Let’s jump in! What You’ll Need An AWS account An S3 bucket with a CSV file uploaded AWS credentials (access key and secret key) Python 3.x installed boto3 and pandas libraries installed (you can install them via pip) pip install boto3 pandas Step-by-Step: Read CSV from S3 Let’s say your S3 bucket is named my-data-bucket , and your CSV file is sample-data/employees.csv . ✅ Step 1: Import Required Libraries import boto3 import pandas as pd from io import StringIO boto3 is...
Post a Comment

Top features of Apache Avro in Hadoop eco-System

-
Avro defines a data format designed to support data-intensive applications, and provides support for this format in a variety of programming languages.

 The Hadoop ecosystem includes a new binary data serialization system — Avro. 

Avro provides:
·     Rich data structures.

·         A compact, fast, binary data format.
·         A container file, to store persistent data.
·         Remote procedure call (RPC).
·       Simple integration with dynamic languages. Code generation is not required to read or write data files nor to use or implement RPC protocols. Code generation as an optional optimization, only worth implementing for statically typed languages.

Its functionality is similar to the other marshaling systems such as Thrift, Protocol Buffers, and so on.

The main differentiators of Avro include the following:

[Hadoop Interview Questions]
[Hadoop Interview Questions]
Dynamic typing — The Avro implementation always keeps data and its corresponding schema together. As a result, marshaling/unmarshaling operations do not require either code generation or static data types. This also allows generic data processing.

Untagged data — Because it keeps data and schema together, Avro
marshaling/unmarshaling does not require type/size information or manually assigned IDs to be encoded in data. As a result, Avro serialization produces a smaller output.

Enhanced versioning support — In the case of schema changes, Avro contains both schemas, which enables you to resolve differences symbolically based on the field names.
Because of high performance, a small codebase, and compact resulting data, there is a wide adoption of Avro not only in the Hadoop community, but also by many other NoSQL implementations (including Cassandra).

At the heart of Avro is a data serialization system. Avro can either use reflection to dynamically generate schemas of the existing Java objects, or use an explicit Avro schema — a JavaScript Object Notation (JSON) document describing the data format. Avro schemas can contain both simple and complex types.

Simple data types supported by Avro include null, boolean, int, long, float, double, bytes, and string. Here, null is a special type, corresponding to no data, and can be used in place of any data type.

Complex types supported by Avro include the following:
Record — This is roughly equivalent to a C structure. A record has a name and optional namespace, document, and alias. It contains a list of named attributes that can be of any Avro type.
Enum — This is an enumeration of values. Enum has a name, an optional namespace, document, and alias, and contains a list of symbols (valid JSON strings).
Array — This is a collection of items of the same type.
Map — This is a map of keys of type string and values of the specified type.
Union — This represents an or option for the value. A common use for unions is to specify nullable values.

Related: Top 100 HADOOP Interview Questions-Part:1

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

Big Data: Top Cloud Computing Interview Questions (1 of 4)

-
Image
The below are frequently asked interview questions on Cloud computing: 1) What is the difference between Cloud and Grid? Grid: -Information service -Security Service -Data management -Execution Manageement Cloud: - Maintains up-to-date information of resources -Create VMs according to user requirement -Application deploment -User management 2) What are the different cloud standards? -Interoperability standards -Security standards -Portability Standards -Governance and Risk standards 3) What are the two different sub-systems in Cloud computing ? -Management sub system -Resource sub system 4)What is Cloud compouting? The promise of cloud computing is ubiquitous access to a broad set of applications and services, which are delivered over the network to multiple customer. 5) Why we need specialized network for Cloud services? The public Internet is the simplest choice for delivering cloud-based services. In this model, the cloud provider simply purchases Inter...
Read more