Featured Post

14 Top Data Pipeline Key Terms Explained

-
Image
 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
Post a Comment

Here's Quick Guide on Hadoop Security

-
Here is a topic of security and tools in Hadoop. These are security things that everyone needs to take care of while working with the Hadoop cluster.


Quick guide on Hadoop security and its features with top references.

Hadoop Security

Security

  • We live in a very insecure world. For instance, your home's front door to all-important virtual keys, your passwords, everything needs to be secured. In Big data systems, where humongous amounts of data are processed, transformed, and stored. So security you need for the data.
  • Imagine if your company spent a couple of million dollars installing a Hadoop cluster to gather and analyze your customers' spending habits for a product category using a Big Data solution. Here lack of data security leads to customer apprehension.

Security Concerns

  • Because that solution was not secure, your competitor got access to that data, and your sales dropped 20% for that product category.
  • How did the system allow unauthorized access to data? Wasn't there any authentication mechanism in place? Why were there no alerts?
  • This scenario should make you think about the importance of security, especially where sensitive data is involved.
  • Hadoop has inherent security concerns due to its distributed architecture. The installation that has clearly defined user roles and multiple levels of authentication (and encryption) for sensitive data will not let any unauthorized access go through.

Hadoop Security.

  • When talking about Hadoop security, you have to consider how Hadoop conceptualized. When Doug Cutting and Mike Cafarella started developing Hadoop, security was not the priority.
  • Hadoop meant to process large amounts of web data in the public domain, and hence security was not the focus of development. That's why it lacked a security model and only provided basic authentication for HDFS—which was not very useful since it was easy to impersonate another user.
  • Another issue is that Hadoop was not designed and developed as a cohesive system with pre-defined programs. But it was as a collage of modules that either correspond to various open-source projects or a set of (proprietary) extensions developed by different vendors to supplement functionality lacking within the Hadoop ecosystem.
  • Therefore, Hadoop expects a secure environment for data processing. In real-time, there are some glitches to have secure processing. You can read more about it from references.

Keep Reading
White Papers

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

-
Image
Here's a quick resolution for import datetime Python error . The reason is your .py python script name and datetime  are the same. I'll show you how this error happens and its resolution.   Here's the Resolution for ImportError I've created a script called 'datetime.py.' to check whether the minute value is 'odd' or not. During the import of my python script, I got the import error cannot import name datetime. My Script: datetime.py My script's intention is to find whether the minute value is odd or not. Python Logic from datetime import datetime odds = [ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , 51 , 53 , 55 , 57 , 59 ] right_this_minute = datetime.today().minute if right_this_minute in odds: print ( "This minute seems a little odd." ) else : print ( "Not an odd minute." ) ImportError I have imported my datetime.py from Linux. It gives an error ...
Read more

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

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