Integrate AWS DynamoDB with Spring BootTech by Sunny Srinidhi - June 26, 2019March 12, 20200 Here is another POC to add to the growing list of POCs on my Github profile. Today, we’ll see how to integrate AWS DynamoDB with a Spring Boot application. This is going to be super simple, thanks to the AWS Java SDK and the Spring Data DynamoDB package. Let’s get started then. Dependencies First, as usual, we need to create a Spring Boot project, the dependencies of which look like: <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> </dependency> <dependency> <groupId>com.amazonaws</groupId> <artifactId>aws-java-sdk-dynamodb</artifactId> <version>1.11.573</version>
Connect Apache Spark to your HBase database (Spark-HBase Connector)Data ScienceTech by Sunny Srinidhi - April 1, 2019January 31, 20202 There will be times when you’ll need the data in your HBase database to be brought into Apache Spark for processing. Usually, you’ll query the database, get the data in whatever format you fancy, and then load that into Spark, maybe using the `parallelize()`function. This works, just fine. But depending on the size of the data, this could cause delays. At least it did for our application. So after some research, we stumbled upon a Spark-HBase connector in Hortonworks repository. Now, what is this connector and why should you be considering this? The Spark-HBase Connector (shc-core) The SHC is a tool provided by Hortonworks to connect your HBase database to Apache Spark so that you can tell your Spark context to pickup the
How you can improve your backend services’ performance using Apache KafkaTech by Sunny Srinidhi - November 27, 2018February 25, 20201 In most real world applications, we have a RESTful API service facing various client applications and a collection of backend services which process the data coming from those clients. Depending on the application, the architecture might have various services spread across multiple clusters of servers, and some form of queue or messaging service gluing them together. Today, we're going to talk about one such messaging service - Apache Kafka - and how it can improve the performance of your services. We're going to assume that we have at least two microservices, one for the APIs that are exposed to the world, and one which processes the requests coming in from the API microservice, but in an async fashion. Because this is
Why you should switch to Signal or Telegram from WhatsApp, TodayTech by Sunny Srinidhi - November 23, 2018December 19, 20193 When we think of communicating with someone today, we mostly think of sending them a text message or a voice note on WhatsApp. And some other people who are least bothered about their privacy online, think of Facebook Messenger. But not all these users know what's happening with the messages they exchange on these platforms. Let's take a look at that. Before we start, let me admit, I am by no means an expert on security and privacy online. But I have done enough research for the last couple of years, which made me switch to Firefox and DuckDuckGo (with a lot of customized preferences on both), from Google's Chrome browser and search. I've made a lot of other such switches
Keystroke Dynamics, What Is It?Tech by Sunny Srinidhi - November 16, 20180 For decades, we have been using the two-pronged key system for securing our electronic data and services. The two-pronged key we're talking about is the username/password combination. There are variations of this, of course. For example, instead of a username, you might be using your email address, or something called a user ID. But the concept remains the same. The username/password combination for security is over 50 years old. To be more precise, it was first implemented in the year 1961 at Massachusetts Institute of Technology (MIT). We have been using this security method for all kinds of data and services online, including but not limited to emails, banking, and gaming services. But it's also true that it's been proved a lot many
What is multicollinearity?Data Science by Sunny Srinidhi - August 8, 2018January 30, 20200 Multicollinearity is a term we often come across when we’re working with multiple regression models. But do we actually know what it means?
Overfitting and Underfitting models in Machine LearningData Science by Sunny Srinidhi - August 2, 20180 In most of our posts about machine learning, we've talked about overfitting and underfitting. But most of us don't yet know what those two terms mean. What does it acutally mean when a model is overfit, or underfit? Why are they considered not good? And how do they affect the accuracy of our model's predictions? These are some of the basic, but important questions we need to ask and get answers to. So let's discuss these two today. The datasets we use for training and testing our models play a huge role in the efficiency of our models. Its equally important to understand the data we're working with. The quantity and the quality of the data also matter, obviously. When the data
Different types of Validations in Machine Learning (Cross Validation)Data Science by Sunny Srinidhi - August 1, 20180 Now that we know what is feature selection and how to do it, let's move our focus to validating the efficiency of our model. This is known as validation or cross validation, depending on what kind of validation method you're using. But before that, let's try to understand why we need to validate our models. Validation, or Evaluation of Residuals Once you are done with fitting your model to you training data, and you've also tested it with your test data, you can't just assume that its going to work well on data that it has not seen before. In other words, you can't be sure that the model will have the desired accuracy and variance in your production environment. You need
Different methods of feature selectionData Science by Sunny Srinidhi - July 31, 2018November 6, 20191 In our previous post, we discussed what is feature selection and why we need feature selection. In this post, we're going to look at the different methods used in feature selection. There are three main classification of feature selection methods - Filter Methods, Wrapper Methods, and Embedded Methods. We'll look at all of them individually. Filter Methods Filter methods are learning-algorithm-agnostic, which means they can be employed no matter which learning algorithm you're using. They're generally used as data pre-processors. In filter methods, each individual feature in the dataset will be scored on its correlation with the dependent variable. A variety of statistical tests will be used to calculate this correlation score. Based on this score, it will be decided whether to
Linear Regression in Python using SciKit LearnData Science by Sunny Srinidhi - July 30, 2018July 30, 20181 Today we'll be looking at a simple Linear Regression example in Python, and as always, we'll be using the SciKit Learn library. If you haven't yet looked into my posts about data pre-processing, which is required before you can fit a model, checkout how you can encode your data to make sure it doesn't contain any text, and then how you can handle missing data in your dataset. After that you have to make sure all your features are in the same range for the model so that one feature is not dominating the whole output; and for this, you need feature scaling. Finally, split your data into training and testing sets. Once you're done with all that, you're ready to start your