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5 Tips for System Design Interviews
Here are 5 Tips for System Design interviews. They are helpful when preparing for a System Design interview.
1. Don’t get into details prematurely
2. Avoid fitting requirements to a set architecture in mind
3. Keep it simple, stupid! Remember to look at the big picture and avoid too many hacks when solving.
4. Have justifications for the points you make. Don’t use buzz words or half hearted thoughts in your design.
5. Be aware of the current solutions and tech practices. A lot of solutions can be purchased off the shelf which simplify implementation. You should be able to argue for a custom implementation with it’s pros and cons.
Have these on the back of your mind during you interview, and all the best!
Here are three major points evaluated during the interview:
1. Clarity of Thought
a. Express your thoughts in a clear manner.
b. Justify your decisions. Critical reasoning and argument are key to a successful software design.
c. When faced with a problem, use standard approaches to mitigate it. For example, say you are faced with an availability problem. State that replication and partitioning help increase availability in general, and move on to offer a solution.
d. Don’t make points without thinking them through. Half-hearted attempts at solving problems are frowned upon heavily.
2. Know about existing solutions
a. Stay up to date with the current solutions in the market. This includes products and design practices. If NoSQL is being adopted left right and center, you need to be aware of it.
b. Know when to pick a solution vs. building something custom. If you name a product, you should be (generally) aware of the features it provides.
c. Design practices enable you to meet custom requirements. Examples are decoupling systems, load balancing, sticky sessions, etc…
a. Switch your targets as the requirements shift. If the interviewer wants to know about one particular part of the system, do it first.
b. Never have a set architecture in mind. We all try to fit requirements to a system, but only after it has been shaped by the initial ones. A rigid attitude creates a brittle architecture. It will break before you do.
c. Take a step back at times to make adjustments to the general architecture. Being focused on one part can narrow our vision and bloat those areas. There will be components which can be extracted out and extended to the rest of the system.
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Big Data In 5 Minutes | What Is Big Data?| Introduction To Big Data |Big Data Explained |Simplilearn
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This video will help you understand what Big Data is, the 5V’s of Big Data, why Hadoop came into existence, and what Hadoop is. You will also learn about the storage unit and processing unit of Hadoop, and the implementation of Big Data through use cases. In the end, we will have a quiz on Hadoop. A massive amount of data that cannot be stored, processed, and analyzed using the traditional ways is known as Big Data. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let’s deep dive into this video and understand Big Data and Hadoop.
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Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. With our online Hadoop training, you’ll learn how the components of the Hadoop ecosystem, such as Hadoop 3.4, Yarn, MapReduce, HDFS, Pig, Impala, HBase, Flume, Apache Spark, etc. fit in with the Big Data processing lifecycle. Implement real life projects in banking, telecommunication, social media, insurance, and e-commerce on CloudLab.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
Who should take up this Big Data and Hadoop Certification Training Course?
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Understand Your Viewership Trends: New & Returning Viewers in YouTube Analytics
Knowing what videos to create can be a tough decision as a creator. Do you try to please your existing audience? Or do you try to reach out to new viewers? Or, can you do both in the same video? Today, we walk you through 2 metrics in YouTube Analytics that can help you better understand your audience – Returning & New Viewers. In this video we’ll cover what these audience metrics are, how to understand these metrics and the trends on your channel, and ways you can act on this information when it comes to making videos.
0:00 Intro to Returning & New Viewers
0:33 What & Where are these metrics?
1:37 Understanding Trends: More Returning Viewers
2:55 Understanding Trends: More New Viewers
4:40 Understanding Trends: Mix of New & Returning Viewers
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