Featured Post

PowerCurve for Beginners: A Comprehensive Guide

-
Image
PowerCurve is a complete suite of decision-making solutions that help businesses make efficient, data-driven decisions. Whether you're new to PowerCurve or want to understand its core concepts, this guide will introduce you to chief features, applications, and benefits. What is PowerCurve? PowerCurve is a decision management software developed by Experian that allows organizations to automate and optimize decision-making processes. It leverages data analytics, machine learning, and business rules to provide actionable insights for risk assessment, customer management, fraud detection, and more. Key Features of PowerCurve Data Integration – PowerCurve integrates with multiple data sources, including internal databases, third-party data providers, and cloud-based platforms. Automated Decisioning – The platform automates decision-making processes based on predefined rules and predictive models. Machine Learning & AI – PowerCurve utilizes advanced analytics and AI-driven models ...
Post a Comment

Top Hadoop Architecture Interview Questions

-
The hadoop.apache.org web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage.

The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly.

More : Top selected Hadoop Interview Questions

Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode.

Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm

HDFS


HDFS is a distributed file system layer that sits on top of the native file system for an operating system. For example, HDFS can be installed on top of ext3, ext4, or XFS file systems for the Ubuntu operating system.

It provides redundant storage for massive amounts of data using cheap, unreliable hardware. At load time, data is distributed across all the nodes. That helps in efficient MapReduce processing. HDFS performs better with a few large files (multi-gigabytes) as compared to a large number of small files, due to the way it is designed.

Files are "write once, read multiple times." Append support is now available for files with the new version, but HDFS is meant for large, streaming reads—not random access. High sustained throughput is favored over low latency.

Files in HDFS are stored as blocks and replicated for redundancy or reliability. By default, blocks are replicated thrice across DataNodes; so three copies of every file are maintained. Also, the block size is much larger than other file systems. For example, NTFS (for Windows) has a maximum block size of 4KB and Linux ext3 has a default of 4KB.

Compare that with the default block size of 64MB that HDFS uses.


Name Node



NameNode (or the "brain") stores metadata and coordinates access to HDFS. Metadata is stored in NameNode's RAM for speedy retrieval and reduces the response time (for NameNode) while providing addresses of data blocks. 

This configuration provides simple, centralized management—and also a single point of failure (SPOF) for HDFS. In previous versions, a Secondary NameNode provided recovery from NameNode failure; but current version provides capability to cluster a Hot Standby (where the standby node takes over all the functions of NameNode without any user intervention) node in Active/Passive configuration to eliminate the SPOF with NameNode and provides NameNode redundancy.

Since the metadata is stored in NameNode's RAM and each entry for a file (with its block locations) takes some space, a large number of small files will result in a lot of entries and take up more RAM than a small number of entries for large files.

Also, files smaller than the block size (smallest block size is 64 MB) will still be mapped to a single block, reserving space they don't need; that's the reason it's preferable to use HDFS for large files instead of small files.

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