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

Big Data: IBM InfoSphere BigInsights Basics

-
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)

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