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How To Prepare for Programming and Coding Interviews? Topics and Skills

There is no doubt cracking the Coding interviews of top tech companies like Google, Microsoft, and Amazon is very hard, but you can still crack it by carefully planning and preparing the essential skills which matter most like Data structures and Algorithms, System Design, Multithreading and Concurrency, SQL and Database design, and of course, problem-solving skills.

Being an author of Java and a programming blog, I often receive queries about how to prepare for coding interviews? How to pass Google or Microsoft Software Developer interviews? Or how can I get a software development job on Amazon or Facebook?

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The Bag Data Structure From Eclipse Collections

In computer science, a bag is defined as an abstract data structure, that allows keeping duplicate elements in any order. This is similar to a physical bag, where you could also put any elements and take them out randomly. So, bags are different from lists (because lists care about a particular position of an element) and from sets (because sets do not allow duplicates). The bag is a good choice when you just need to collect items and do some processing using iterations. Java does not offer its "vanilla" implementation of the bag, however, you could find it in popular collections libraries. In this post, we will review the Bag from Eclipse Collections, which supplies both mutable and immutable versions.

Create Bags

Before we will proceed with various Bag methods, let observe how to initialize a new Bag instance within the Eclipse Collections framework. Likewise to other types of collections, there are presented both mutable (modifiable) and immutable (non-modifiable) versions. In general, we can use the Bags class, which allows utilizing static factory methods to obtain bags:

  • Bags.immutable.* calls ImmutableBagFactory to create immutable bags.
  • Bags.mutable.* calls MutableBagFactory to create mutable bags.

Both types use the same approaches, that can be separated into the following three categories:

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An Overview of the Priority Queue

The priority queue is a somewhat similar data structure to the queue. The difference lies in how the elements are being processed:

  • A standard queue strictly follows the FIFO (First-In-Last-Out) principle.
  • A priority queue does not follow the FIFO principle.

In a priority queue, the elements are being removed from the queue based on their priority. This translates to the requirement that:

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Data Lake vs Data Warehouse: Do You Need Both?

Most enterprises today have a data warehouse in place that is accessed by a variety of BI tools to aid in the decision-making process. These have been in use for several decades now and have served enterprise data requirements quite well. 

However, as the volume and types of data being collected expand, there’s also a lot more that can be done with that data. Most of these are use cases that an enterprise might not even have identified yet, and they won’t be able to do that until they have had a chance to actually play around with the data. 

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Unstructured Data Is an Oxymoron

Strictly speaking, “unstructured data” is a contradiction in terms. Data must have structure to be comprehensible. By “unstructured data” people usually mean data with a non-tabular structure.

Tabular data is data that comes in tables. Each row corresponds to a subject, and each column corresponds to a kind of measurement. This is the easiest data to work with.