Featured Post

8 Ways to Optimize AWS Glue Jobs in a Nutshell

-
Image
  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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

How to Check Kafka Available Brokers

-
Image
Here's the command to check the list of brokers present in a Kafka Cluster. You can say the Broker is the heart of the Kafka cluster. In simple terms, it works as a process. The main function is to receive messages from publishers and gives permission to access messages by consumers. How to Check Available Brokers in Kafka Here is the command: linux$  ./zookeeper-shell.sh zookeeper-IPaddress:2181 | "ls /brokers/ids" Just use the above command to get the number of brokers present in your host (Kafka Cluster). What is Default Broker-id in Kafka  The default broker id is -1 . When you create a new broker, it adds to -1. Then, it gives a new broker id. The broker ids will be generated from  reserved.broker.max.id + 1. Types of broker ids. Use assigned Zookeeper generated broker id. According to Wiki: Kafka runs on a cluster of one or more servers (called brokers), and the partitions of all topics are distributed across the cluster nodes.  Additionally, partitions are replicat
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 or
Read more