If you have come here, then you probably know that current version of Apache Flink 1.16 Scala API still depends on Scala 2.12. However, the good news is that previous Flink release 1.15 introduced an important change for Scala users, which allows us to use own Scala version via Flink Java API. That means users can now use Scala 2.13 or 3 to write Flink jobs. Earlier, Flink had Scala 2.12 as a mandatory library on application classpath, so one could not use newer version due to version conflicts.

Official Flink Scala API will be still available in future, but will probably be deprecated at some point. It is unclear at this point. Flink community made a decision to not engage with newer Scala version and make Flink to be Scala-free, in terms of user's Scala version choice. Whether it is good or bad for Scala users in general we will going to see in near future. Definitely this choice is good, as it unlocks Flink for newer Scala versions.

Context

As part of my role of Data Architect at work, I often deal with AWS data services to run Apache Spark jobs such as EMR and Glue ETL. At the very beginning team needed to choose Spark supported programming language and start writing our main jobs for data processing.

Before we further dive into the languages choice, let's quickly remind what is Spark for EMR. Glue ETL is going to be skipped from the blog-post.

Apache Spark is one of the main component of AWS EMR, which makes EMR still meaningful service to be used by Big Data teams. AWS EMR team is building its own Spark distribution to integrate it with other EMR applications seamlessly. Even though Amazon builds own Spark, they keep the same Spark version, which is equal to open source version of Spark. All features of Apache Spark are available in EMR Spark. EMR allows to run a Spark application in EMR cluster via step type called “Spark Application”.

Change Data Capture (CDC) is a popular technique for replication of data from OLTP to OLAP data store. Usually CDC tools integrate with transactional logs of relational databases and thus are mainly dedicated to replicate all possible data changes from relational databases. NoSQL databases are usually coming with built-in CDC for any possible data change (insert, update, delete), for example AWS DynamoDB Streams.

In this blog-post, we will look at Delta Lake table format, which supports "merge" operation. This operation is useful when we need to update replicated data in Data Lake.

Cropped view of one the region in the middle of the tree we will build further

Decision Tree classifier is one the simplest algorithm to implement from scratch. One of the benefit of this algorithm is it can be trained without spending too much efforst on data preparation and it is fast comparing to more complex algorithms like Neural Networks. In this blog post we are going to implement CART algorithm, which stands for Classification and Regression trees. There are many other algorithms in decision trees space, but we will not describe them in this blog post.

Data science practitioners often use decision tree algorithms to compare their performance with more advanced algorithms. Although decision tree is fast to train, its accuracy metric usually lower than accuracy on the other algorithms like deep feed forward networks or something more advanced using the same dataset. However, you do not always need high accuracy value, so using CART and other decision tree ensemble algorithms may be enough for solving particular problem.

Kubernetes has built-in controllers to handle its native resource such as

• Pod
• Service
• Deployment
• etc.

What if you want a completely new resource type, which would describe some new abstraction in clear and concise way? Such new resource would describe everything in one single type which would require 5-10 separate native Kubernetes resources.

Face detection and recognition is one the area where Deep Learning is incredibly useful. There are many studies and datasets related to human faces and their detection/recognition. In this article we will implement Machine Learning pipeline for face detection and recognition using few libraries and CNN model.

Last time we used ANN to train a Deep Learning model for image recognition using MNIST dataset. This time we are going to look at more advanced network called Convolutional Neural Network or CNN in short.

CNN is designed to tackle image recognition problem. However, it can be used not only for image recognition. As we have seen last time, ANN using just hidden layers can learn quite well on MNIST. However, for real life use cases we need higher accuracy. The main idea of CNN is to learn how to recognise object in their different shapes and positions using specific features of the image data. The goal of CNN is better model regularisation by using convolution and pooling operations.

Deep Feed Forward Neural Network is one of the type of Artificial Neural Networks, which is also able to classify computer images. In order to feed pixel data into the neural net in RBG/Greyscale/other format one can map every pixel to network inputs. That means every pixel becomes a feature. It may sound scary and highly inefficient to feed, let's say, 28 hieght on 28 width image size, which is 784 features to learn from. However, neural networks can learn from the pixel data successfully and classify unseen data. We are going to prove this.

Please note, there are additional type of networks which are more efficient in image classification such as Convolutional Neural Network, but we are going to talk about that next time.

Dataset

Adam is one more optimization algorithm used in neural networks. It is based on adaptive estimates of lower-order moments. It has more hyper-parameters than classic Gradient Descent to tune externally

Good default settings for the tested machine learning problems are:

• α = 0.001, // learning rate. We have already seen this one in classic Gradient Descent.
• β₁ = 0.9,
• β₂ = 0.999
• eps = 10−8.

In this article we are going to use Scala mini-library for Deep Learning that we developed earlier in order to study basic linear regression task. We will learn model weights using perceptron model, which will be our single unit network layer that emits target value. This model will predict a target value yHat based on two trained parameters: weight and bias. Both are scalar numbers. Weights optimization is going to be based on implemented Gradient descent algorithm:

• Gradient Descent Optimization
• Training Loop

Model equation:

y = bias + weight * x