EXPERT KNOWLEDGE AT A GLANCE

Month: January 2021

Supervised vs Unsupervised vs Reinforcement Learning

Supervised vs Unsupervised vs Reinforcement Learning – The three main categories of machine learning. Why these boundaries have been drawn and what they look like will be discussed in this article. The knowledge about this is an elementary part to understand machine learning correctly and to be able to apply it to data in a meaningful way.

This figure contrasts Supervised vs Unsupervised vs Reinforcement Learning.
Supervised vs Unsupervised vs Reinforcement Learning – Overview

Supervised vs Unsupervised vs Reinforcement Learning – Machine Learning Categories

Machine learning is a branch of artificial intelligence. While AI deals with the functioning of artificial intelligence and compares it with the functioning of the human brain, machine learning is a collection of mathematical methods of pattern recognition. If you want to know more about the differences between Machine Learning, AI and Deep Learning, read our article on the subject. IT systems should be given the ability to automatically learn from experience and improve. Algorithms play a central role here. These can be classified into different learning categories.

In the following figures the three main categories of machine learning methods are shown.

This figure shows Supervised vs Unsupervised vs Reinforcement Learning in the machine learning context.
Supervised vs Unsupervised vs Reinforcement Learning – Machine Learning Context

In the meantime, there are many more categories, some of which are hybrids of the individual main categories. One example is semi-supervised learning. This is certainly also a major machine learning topic, but has been left out for the time being for the sake of simplicity.

What is supervised learning?

In supervised learning, the machine learning algorithm iteratively learns the dependencies between data points. The output to be learned is specified in advance and the learning process is supervised by matching the predictions. How the The optimized algorithm is to apply the learned patterns to unknown data to make predictions.

Supervised vs Unsupervised vs Reinforcement Learning - This figure shows the basic principle of supervised learning.
Supervised vs Unsupervised vs Reinforcement Learning – Supervised Learning

Supervised learning methods can be applied to regression, i.e., prediction, or trend prediction, as well as classification problems.

What is supervised classification?

In classification, abstract classes are formed in order to delimit and order data in a meaningful way. For this purpose, objects are obtained on the basis of certain similar characteristics and structured among each other.

Decision trees can be used as prediction models to create a hierarchical structure, or the feature values can be assigned as class labels and in the form of a vector.

In the following figure the most important supervised classification algorithms are listed.

Supervised vs Unsupervised vs Reinforcement Learning - This figure shows the main algorithms of supervised learning.
Supervised vs Unsupervised vs Reinforcement Learning – Main Algorithms of Supervised Learning.

What is supervised regression?

On the other hand, supervised regression algorithms can be used to make predictions and infer causal relationships between independent and dependent variables.
For example, linear regression can be used to fit the data to a straight line or, conversely, to fit a line to the data object.
We have discussed the exact process of linear regression here in this article.

What is unsupervised learning?

In unsupervised learning, patterns are determined in data without initial patterns and relationships being known.
Especially in complex tasks, these methods can be useful to find solutions that would hardly be solvable by hand. An example is autonomous driving, or large biochemical systems with many interactions.
One key to success is a huge data set. The more data available, the more accurate models can be created.

Supervised vs Unsupervised vs Reinforcement Learning - This figure shows the basic principle of unsupervised learning.
Supervised vs Unsupervised vs Reinforcement Learning – Unsupervised Learning

In unsupervised machine learning methods, two basic principles, which also classify the algorithms used, can be distinguished. The clustering and the dimensional reduction.

What is unsupervised clustering?

The main goal of unsupervised clustering is to create collections of data elements that are similar to each other, but dissimilar to elements in other clusters. The figure below shows some of the main clustering algorithms.

Supervised vs Unsupervised vs Reinforcement Learning - This figure shows the main algorithms of unsupervised learning.
Supervised vs Unsupervised vs Reinforcement Learning – Main algorithms of unsupervised learning.

The clustering algorithms differ primarily in the cluster creation process, but also in the definition of such clusters. Thus, the relationships between clusters can also be used and hierarchical relationships can be explored.

What is unsupervised dimensional reduction?

With a high number of features, high dimensional relations can be translated low dimensional with these transformation methods. The goal is to keep the loss of information as small as possible.
The reduction methods can be divided into two main categories: Methods from linear algebra and from manifold learning.

Manifold learning is an approach to nonlinear dimensionality reduction. Algorithms for this task are based on the idea that they can learn the dimensionality of the data without a given classification and project it in a low-dimensional way.
For example, from the field of linear algebra, matrix factorization methods can be used for dimensionality reduction.

What is reinforcement learning?

In reinforcement learning, a program, a so-called agent, should independently develop a strategy to perform actions in an environment. For this purpose, positive or negative reinforcements are conveyed, which describe the interaction interactions of the agent with the environment. In other words, immediate feedback on an executed task. The program should maximize rewards or minimize punishments. The environment is a kind of simulation scenario that the agent has to explore.
The following figure describes the interactions of all components of a reinforcement learning process.

Supervised vs Unsupervised vs Reinforcement Learning - This figure shows the main principle of reinforcement learning.
Supervised vs Unsupervised vs Reinforcement Learning – Main principle of reinforcement learning.

There are two basic types of reinforcement learning.
Namely, whether the environment is model-based or not.
In model-based RL, the agent uses predictions of the environment response during learning or action.
If no model is available, the data is generated by trial and error.

Apache Spark Streaming – Great and Simple Open Source Solution for Stream Processing

Apache Spark Streaming – Every company produces several million pieces of data every day. Properly analyzed, this information can be used to derive valuable business strategies and increase productivity.
Until now, this data was consumed and stored in a persistent. Even today, this is an important step in order to be able to perform analyses on historical data at a later date. Often, however, analysis results are desired in real time. Be it only reference values that have been exceeded.


So-called data streams, i.e. data that is continuously generated from thousands of data sources, can already be consumed before they end up in a persistence, without the flow rate being significantly reduced. It is even possible to train neural networks using such a stream.


In this article, we’ll tell you why you shouldn’t miss out on Apache Spark and Apache Spark Streaming if you’re planning to integrate stream processing in your organization.

What is Apache Spark?


Apache Spark has become one of the most important and performant unified data analytics on the market today. The framework provides a total solution of data processing and AI integration. This allows companies to easily develop performant data pipelines and train AI methods using massive data streams.


Apache Spark combines several partially interdependent components. So can be deployed in a modular fashion to a certain extent.
Spark can run in its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos or on Kubernetes.
The data here can come from streaming sources, such as Kafka, as well as static data sources. So far, the programming languages Java, Scala, Python and R are supported. These are currently the most commonly used languages across all scientific disciplines for implementing data analysis methods.

What does a Spark cluster look like?

The coordinator of a Spark program on a cluster is the so-called SparkContext object. This controls the individual Spark applications as they run as independent processes.
The Coordinator then connects to the Central Element, a Cluster Manager, which then allocates resources to the individual applications.
The figure below shows an example of a typical Spark cluster with all its components.

The figure  shows an example of a typical Spark cluster with all its components.
Overview Apache Spark Cluster

The actual calculations and data storage then take place on the nodes. These processes, also called executors, then execute tasks and hold the data in memory or disk space. The cache can then be accessed by another node.

Apache sparks underlying technology – The key to high Performance

Spark Core is the underlying unified computing engine on which all Spark functions are built. It enables parallel processing even for large datasets and thus ensures very high-performance processes.
The following figure shows how the Apache Spark Core APIs are composed.

The  figure shows how the Apache Spark Core APIs are composed.
Apache Spark Core APIs

The core API consists of low level APIs, where object manipulation via Resilient Distributed Datasets (RDDs) takes place and structured APIs, where all data types are manipulated and batch or streaming jobs take place.

How do the individual Apache Spark APIs work?

In order to properly understand the API structure, its components must be placed in a historical context.

The figure shows the development history of the Apache Spark APIs.
Development history of the Apache Spark APIs

What is the RDD API?

The RDD (Resilient Distributed Dataset) API has been implemented since the first Spark release and is based on the Scala collections API.
RDDs are a set of Java or Scala objects that represent data and thus are the building blocks of Spark. They excel in being compile-time type-safe and inert.

All higher level APIs can be decomposed into RDDs. Various transformations can be performed in parallel using this API. Each of them defines an operation to be executed, which is invoked by calling an action method and creates a new RDD. This then represents the transformed data.

What is the Dataframe API?

The Dataframe API introduces a higher level abstraction. Spark dataframes correspond to the Pandas dataframes structure. They are built on top of RDDs and represent two-dimensional data and a schema. It contains an ordered collection of columns and each different column can consist of different data types. Each value is unique by a row and a column index.


When data is transferred between nodes, only the data is transferred. The metadata is managed in a schema registry separate from spark. This has significantly improved the performance and scalability of Spark.
The API is suitable for creating a relational query plan. Thus, manipulation of data can now be done using a query language.

What is the Dataset API?

When working with dataframes, compile-time type safety is lost. This is a strength of the RDD API. The Dataset-API was created to combine the advantages of both APIs. It is thus the second most important Spark API next to the RDD API.


The basis of this API are integrated encoders, which are responsible for the conversion between JVM objects and the internal Spark SQL representation.

What components does Apache Spark consist of?

Spark is modularly extensible through the use of components. Spark includes libraries for various tasks ranging from SQL to streaming and machine learning. All components are based on the Spark Core, the foundation for parallel and distributed processing of large data sets. How this API looks in detail and what makes it so performant, we will explain later.
The following figure lists the individual Apache Spark components.

In the figure, the ecosystem of Apache Spark is shown with all the major components.
Apache Spark Ecosystem

Apache Spark Spark SQL

With this component RDDs are converted into the so-called data frames, i.e. provided with metadata information.
The whole thing is done by a catalyst optimizer, which executes an execution plan in the form of a tree.

Apache Spark GraphX

This framework can be used to perform high-performance calculations on graphs. These operations can run in parallel.

Apache Spark MLlib/SparkML

With the MLlib component, machine learning pipelines can be constructed very easily. For this purpose, ready-made models and common machine learning algorithms (classification, regression, clustering …) can be used. Thus, data identification, feature extraction and transformation are combined in a unified framework.

Apache Spark Streaming

Apache Spark Streaming enables and controls the processing of data streams. However, Apache Spark Streaming can also process data from static data sources.
In the case of datastreaming, input stream goes from a streaming data source, such as Kafka, Flume or HDFS, into Apache Spark Streaming.
There, it is broken into batches and fed into the Spark engine for parallel processing. The final results can then be output to HDFS databases and dashboards.
The following figure illustrates the principle of Apache Spark Streaming.

The figure illustrates the principle of Apache Spark Streaming.
Principle of Apache Spark Streaming

All components can consume directly from the stream via Apache Spark Streaming. This component takes a crucial role here. It coordinates the requests via sliding window operations and regulates the data flow. Since all components are based on the Spark Core API, absolute compatibility is guaranteed. Especially in the Big Data area, this can deliver a decisive performance bonus.

What are Messaging Patterns?

Messaging Patterns- What are they? What are their strengths and why should they only be used with caution? We clarify these questions in this article.

What are Design Patterns?


Technology-independent designs can provide proven pattern solutions in software development, ensuring standardized and robust architecture.
If you’ve never heard of software design patterns, check out this article from us on the subject first.


Design patterns allow a developer to draw on the experience of others. They offer proven solutions for recurring tasks. A one-to-one implementation is not advisable. The patterns should rather be used as a guide.

What is a message?

A basic design pattern is the message. Actually a term that is used by everyone as a matter of course, but what is behind it?


Data is packaged in messages and then transmitted from the sender to the receiver via a message channel. The following figure shows such a messaging system.

Messaging Patterns - This scheme shows the basic concept of a message
Messaging Patterns – Basic Concept of a Message

The communication is asynchronous, which means that both applications are decoupled from each other and therefore do not have to run simultaneously. The sender must build and send the message, while the receiver must read and unpack it.

What are Messaging Patterns?

However, this form of message transmission is only one way of transferring information. The following figure shows the basic concepts of messaging design patterns.

This diagram shows all the basic components of the messaging design patterns
Basic Components of the Messaging Patterns

What is Message Construction?

It is not enough to decide to use a message. A message can be constructed according to different architectural patterns, depending on the functions to be performed.


The following figure shows some of these patterns.

Messaging Design Patterns - This diagram shows the different Patterns of message construction.
Messaging Design Patterns – Message Construction Patterns

Message Construction – When do I use it?

Massaging can be used not only to send data between a sender and receiver, but also to call a procedure or request a response in another application.


With the right message architecture a certain flexibility can be guaranteed. This makes the message much more robust against possible future changes.

What is Message Routing?

A message router connects the message channels in a messaging system. We will come back to this topic later. This router corresponds to a filter, which regulates the message forwarding, but does not change the message. A message is only forwarded to another channel if all predefined conditions are met.


The following figure lists some specific message router types.

Messaging Design Patterns - This diagram shows the different patterns of message routing.
Messaging Patterns – Message Routing Patterns

When do I use message routing and how?

For example, messages can be forwarded to dynamically defined recipients, or message parts can be processed or combined in a differentiated manner.

What are Messaging Channels?

In a messaging system, the exchange of information does not just happen unregulated. The sender transfers the message to a so-called messaging channel and the receiver requests a specific message channel.
In this way, the sender and receiver are decoupled. However, the sender can determine which application receives the data without knowing about it by selecting the specific messaging channel.


However, the right choice of message channel depends on your architecture. Which channel should be addressed and when?

The following figure lists some such channel types.

Messaging Design Patterns - This diagram shows the different patterns of message channels.
Messaging Patterns – Message Channel Patterns

What are the basic differences between the channel types ?

Basically, the channel types can be divided into two main types.

A distinction can be made between a point-to-point channel, i.e. one sender and exactly one receiver, and a publish-subscribe channel, one sender and several receivers.

What is a Messaging Endpoint?

In order for a sender or receiver application to connect to the messaging channel, an intermediary must be used. This client is called a messaging endpoint.


The following figure shows the principle of communication via messaging endpoints.

Messaging Design Patterns - Dieses Diagramm zeigt the Basic principle of a message endpoint
Basic principle of a Message Endpoint

On the receiver side, the end point accepts the data to be sent, builds a message from it and sends it via a specific message channel. On the receiver side, this message is also received via an end point and extracted again. An application can access several end points here. However, an endpoint can only implement one alternative.

The following figure lists some endpoint types.

Messaging Design Patterns - This diagram shows the different patterns of message endpoints.
Messaging Design Patterns – Message Endpoint Patterns

When do I choose which endpoint?

Receiving messages in particular can become difficult and lead to server overload. Therefore, control and possible throttling of the processing of client requests is crucial. A proven means is, for example, the formation of processing queues or a dynamic adjustment of consumers, depending on the volume of requests.

What is Message Transformation?

If the data format has to be changed when data is exchanged between two applications, a so-called message transformation ensures that the message channel is formally decoupled.


This translation process can be understood as two systems running in parallel. The actual message data is separated from the metadata.


The following figure shows some message transformation types.

Messaging Design Patterns - This diagram shows the different patterns of message transformation.
Messaging Design Patterns – Message Transformation Patterns

How do I monitor my messaging system and keep it running?

A flexible messaging architecture unfortunately leads to a certain degree of complexity on the other side. Especially when it comes to integrating many message producers and consumers decoupled from each other in a messaging system, with partly asynchronous messaging, monitoring during operation can become difficult.


For this purpose, system management patterns have been developed to provide the right monitoring tools. The main goal is to prevent bottlenecks and hardware overloads in order to guarantee the smooth flow of messages.


The following figure shows some test and monitoring patterns.

Messaging Design Patterns - This diagram shows the different patterns of message monitoring.
Messaging Design Patterns – Message Monitoring Patterns

What are the basic systems?

With a typical system management solution, for example, the data flow can be controlled by checking the number of data sent and received, or the processing time.


This is contrasted with the actual checking of the message information contained.

PCA vs Linear Regression – Therefore you should know the differences

PCA vs Linear Regression – Two statistical methods that run very similarly. However, they differ in one important respect. What the two methods actually are and what this difference is, we explain to you in the following article.

What is a PCA?

Principal Component Analysis (PCA) is a multivariate statistical method for structuring or simplifying a large data set. The main goal here is the discovery of relationships in 2 or 3 dimensional domain.
This method enjoys great popularity in almost all scientific disciplines and is mostly used when variables are highly correlated.


However, PCA is only a reliable method if the data are at least interval scaled and approximately normally distributed.
Although the variables are adjusted to avoid redundant effects, the error and residual variance of the data are not taken into account.

The following figure shows the basic principle of a PCA. High dimensional data relationships should be represented in a low dimensional way, with as little loss of information as possible.

PCA vs Linear Regression - Figure shows the basic principle of a PCA. High dimensional data relationships should be represented in a low dimensional way, with as little loss of information as possible.
PCA vs Linear Regression – Basic principle of a PCA

The key point of PCA is dimensional reduction. It is to extract the most important features of a data set by reducing the total number of measured variables with a large proportion of the variance of all variables.
This reduction is done mathematically using linear combinations.

What are linear combinations?

PCA works in a purely exploratory way, searching the data for a linear pattern that best describes the data set.
These linear combinations can best be thought of as straight lines between variable values.
In the figure below, the linear combinations have been applied to a data set.

PCA vs Linear Regression -In this scheme the linear combinations have been applied to a data set
Linear combinations

How does the algorithm work?

In the principal component analysis procedure, a set of fully uncorrelated principal components are first generated.
These contain the main changes in the data and are also known as latent variables, factors or eigenvectors.
The number of extracted components is given here by the data.

The first principal component is formed by minimizing the sum of squared variances of all variables.
During extraction, the variance component is maximized over all variables.
Then, the remaining variance is gradually resolved by the second component until the total variance of all data is explained by the principal components.

The first factor always points in the direction of the maximum variance in the data.
The second factor must be perpendicular to it and explain the next largest variance

PCA vs Linear Regression – How do they Differ?

We have studied the PCA and how it works in great detail. But what are the differences to linear regression?

In the following illustration the main difference is set up against each other.

PCA vs Linear Regression -  The figure shows the main difference between the two methods. The minimization of the error squares to the straight line.
PCA vs Linear Regression – Minimization of the Error Squares to the Straight Line

With PCA, the error squares are minimized perpendicular to the straight line, so it is an orthogonal regression. In linear regression, the error squares are minimized in the y-direction.

Thus, linear regression is more about finding a straight line that best fits the data, depending on the internal data relationships.
Principal component analysis uses an orthogonal transformation to form the principal components, or linear combinations of the variables.

So this difference between the two techniques only becomes apparent when the data are not completely independent, but there is a correlation.

If you want to know more about machine learning methods and how they work, check out our article on the t-SNE algorithm.

What is t-SNE and What are its Great Benefits?

The machine learning algorithm t-Distributed Stochastic Neighborhood Embedding, also abbreviated as t-SNE, can be used to visualize high-dimensional datasets. Each high-dimensional information of a
data point is reduced to a low-dimensional representation. However, the information about existing neighborhoods should be preserved.

So this technique is another tool you can use to create meaningful groups in unordered data collections based on the unifying data properties. If you don’t know what cluster algorithms are, check out this article. Here we present 5 machine learning methods that you should know.
As shown in the following figure, the data should be represented grouped in 2-dimensional space.

The figure shows the data clusters generated by t-Distributed Stochastic Neighborhood Embedding (T-SNE) in 2-dimensional space.
Data clusters generated by t-Distributed Stochastic Neighborhood Embedding (T-SNE)

But how does the algorithm work and what are its strengths? In order to understand its function, we need to look at the origin of the technology.

What is the Stochastic Neighbor Embedding (SNE) Algorithm?

The basis of the t-Distributed Stochastic Neighborhood Embedding algorithm is originally the Stochastic Neighbor Embedding (SNE) algorithm. This converts high-dimensional Euclidean distances into similarity probabilities between individual data points.
The probability with which an object occurs next to a potential neighbor must be calculated.
The dissimilarities between two high-dimensional data points can be explained with a distance matrix, corresponding to the squared Euclidean distance.
A conditional probability is calculated for the low-dimensional correspondence.
This determines the similarity of the two data points on the low-dimensional map.

In order to achieve the closest possible correspondence between the two distributions pij and
qij, a Kullback-Leibler divergence (KL) over all neighbors of each data point is computed as a cost function C. Large costs are incurred for distant data points.

t-Distributed Stochastic Neighborhood Embedding: minimized cost function: sum of the Kullback-Leibler divergences between the original and the induced distribution over the neighbors of an object.
Minimized Cost function: sum of the Kullback-Leibler divergences between the original and the induced distribution over the neighbors of an object.

A gradient descent method is used to optimize the cost function. However, this optimization method converges very slowly. In addition, a so-called crowding problem arises.

If a high dimensional data set is linearly approximated in a small scale, then it cannot be reduced to a lower dimension with a local scaling algo-
rithm to a lower dimension.

What makes the t-Distributed Stochastic Neighborhood Embedding (t-SNE) Algorithmt work?

The t-Distributed Stochastic Neighbor
Embedding (t-SNE) algorithm starts here. On the one hand, a simplified symmetric cost function is used.

The figure shows the simplified symmetric cost function used in t-Distributed Stochastic Neighborhood Embedding.
t-SNE: simplified symmetric cost function

Here, only one KL is minimized over a common probability distribution of all
high, and low dimensional data is minimized.

On the other hand, the similarity of the low-dimensional data points is computed with a Student’s t-distribution and a degree of freedom of one. This can be optimized quickly and is stable to the crowding problem.
stable against the crowding problem.

4 Software Design Patterns Every Programmer Should know

Software Design Patterns – This article is intended to explain the concept of design patterns in a simplified way and to give you an overview of the individual major groups.

Software architecture can be compared to the architecture of a house. So needs the application development in the planning also consists of the design and the construction of a meaningful, stable structure.

During implementation, it is really only about problem definition and solution with the tools given to you. Many of the steps are repetitive and follow routine patterns. The experience of the user or architect plays a major role here.
What do I apply when and how?

What are Software Design Patterns?

For many processes, there are already very optimized, proven templates that can be reused. Through these so-called design patterns, it is therefore possible to indirectly access the experience of others. The concept goes back to the architect Christopher Alexander and was subsequently used by computer scientists as a basis for conceptual design in software architecture.

These Patterns are categorized on the basis their characteristics in so-called Design Pattern Catalogs and logically grouped around a certain clarity to create. These characteristics can be for example pattern similarities among themselves, the applicability, or the consequences. Many literature deal with this classification topic. The categorizations shown in the following figure may therefore differ depending on the point of view.

This diagram shows the 4 Important software design patterns.
4 Important Software Design Patterns.

Creational Patterns

The Creational Design Patterns deal with object and class creation. How can object creations be inherited from other objects and to what extent can classes be instantiated by subclasses? How are these instantiations created and linked?

Patterns should create object creation mechanisms with which object creations are controlled and thus the object is created purposefully on the respective situation. Flexibility and reusability are the intended goals here.
Thereby the construction is separated from the concrete implementation.
In the following scheme some patterns, which are to be assigned to the creational patterns, are represented.

Software Design Patterns - This scheme shows some Creational Patterns examples
Software Design Patterns – Creational Patterns examples

Structural Design Patterns

How do I create large, cohesive, yet efficient structures? How do I properly optimize the interaction of my entities? Structural Design Patterns should help with these questions and standardize the composition of objects and classes. So the focus here is on establishing individual relationships.
The following figure shows some of the patterns assigned here.

Software Design Patterns - This scheme shows some Structural Patterns examples
Software Design Patterns -Structural Patterns examples

It is often a matter of optimizing and saving inheritance processes. For example, objects can be enclosed in a tree structure, which then all use the same interface, or general properties can be moved to a single object, which is then shared by all other objects. Pipelines can be built and process chains can be formed.

Behavioral Patterns

In addition to the efficient assignment and allocation of entities, communication must also be optimized. At this level, the different transfers among them also describe a structural flow of control. These behavioral patterns can be very complex and difficult to grasp, but are determined by how the individual objects are connected to each other.

So how are responsibilities distributed? Behavioral patterns are intended to help increase the flexibility of the software in terms of its behavior in carrying out this communication.
In the following diagram some patterns are represented, which are to be assigned to the Behavioral Patterns.

Software Design Patterns - This scheme shows some Behavioral Patterns examples
Software Design Patterns Behavioral Patterns examples


For example, inheritance between classes is used to distribute behavior between classes. This inheritance is a sequence of different algorithms that retrieve operations in predefined order and are defined, instantiated, and implemented.
Also, behaviors of objects can be encapsulated instead of distributing them across classes. Another behavioral pattern approach is an observer pattern where the dependencies between objects are observed.

Concurrency Patterns

Like also computations at the same time, thus parallel can be executed, so also models can be created parallel.
So whole program instances can be encapsulated as processes and run isolated, or a program can be divided into several threads, which all access the same memory area, but can also work in parallel.
Where which pattern can be used depends on all the workload conditions present and must be carefully coordinated to effectively avoid overload peaks. The following diagram shows some examples of concurrency patterns.

Software Design Patterns - This scheme shows some Concurrency Patterns examples
Software Design Patterns Concurrency Patterns examples

Conclusion

Since not every problem solution has to be developed by oneself, strategically applied design patterns can save time and resources. They can ensure that programs run effectively. A certain standardization is created. This is especially important for cross-team development. A software product is thereby uniformly and coherently conceived and implemented.

Nevertheless, these templates are often criticized. Why is that?
A decisive factor is that design patterns must not be seen as an all-purpose solution. The individual templates must be understood by the developer in order to use them efficiently. Does the template fit my problem 100 percent, or am I creating extra work again?

Design patterns allow you to access the experience of others, but require your own experience in working with these solutions.

If you are interested in more architectural thinking. Here we have put together another interesting software design the Domain Driven Design.

AI vs Machine Learning vs Deep Learning – What is actually what?

It’s almost harder to understand all the acronyms around Artificial Intelligence (AI) than the technology itself.
AI vs Machine Learning vs Deep Learning – These terms are often carelessly mixed together. But what are actually the differences? In this article, we will introduce you to all Three fields, because even though there is overlap, they differ.
It should be important for you to know these differences, as each discipline describes different stages of a data analysis pipeline.

AI vs Machine Learning vs Deep Learning

In the following figure, we have schematically shown you the individual fields in their context. As you can see, the individual disciplines surround each other and form an onion-like layered model.

Schematic representation of ai vs Machine Learning vs  Deep Learning.
AI vs Machine Learning vs Deep Learning – Contextual representation of the AI disciplines

The figure clearly shows that there are relationships between individual disciplines. AI is to be understood as a generic term and thus includes the other fields. The deeper you go in the model, the more specific the tasks become. In the following, we will follow this representation and work our way from the outside to the inside.

Artificial intelligence

All disciplines are encompassed by the term AI. It is a science that explores ways to build intelligent programs and machines that can perceive, reason, act, and solve problems creatively. To this end, it attempts to model how the human brain works.
The following figure shows that AI can basically be divided into two categories.

AI vs Machine Learning vs Deep Learning
Ability and functionally based AI types simply explained
Types of AI

Classification is about measuring the performance of AI based on how well it is able to replicate the human-like brain. In the Based on Functionality category, AI is classified based on how well it matches the human way of thinking. In the second category, it is evaluated based on human intelligence. Within these categories, there are still some subgroups that correspond to an index.

AI vs Machine Learning

So what is the first subcategory Machine Learning and how does it differ from AI?
While AI deals with the functioning of artificial intelligence and compares them with the functioning of the human brain, machine learning is a collection of mathematical methods of pattern recognition. It is about how a system is given the ability to automatically learn and improve from experience. Various algorithms (e.g., neural networks) are used for this purpose. In the following scheme, the broad machine learning field is presented in a categorized way.

AI vs Machine Learning vs Deep Learning
Presentation of all basic machine learning parts
Definition Machine Learning

In machine learning, algorithms are used to build statistical models based on training data. Roughly, these algorithms can be divided into three main learning techniques. While in supervised learning the result is predetermined by a cleanly labeled data set, unsupervised learning is completely self-organized. Here the patterns are to be explored independently.
In reinforcement learning, utility functions are to be independently approximated based on rewards received.

Machine Learning vs Deep Learning/ Deep Neural Learning

Deep learning is a subfield of machine learning similar to machine learning in Ai. Here, multilayer neural networks are used to analyze various factors in large amounts of data. These networks are similar to the human neural system. If you want to know more about this structure, read our article on perceptrons, the smallest unit of a neural network.
Optimization of neural weights, unlike machine learning, can be done using powerful GPUs. Pure machine learning is best used on structured data sets, while for unstructured data you should opt for deep learning. In the following graphic, we have summarized the main factors that make up deep learning. For the network types autoencoder and CNN we provide more detailed articles.

Representation of all basic deep learning components
AI vs Machine Learning vs Deep Learning
Definition Deep Learning

4 Index Data Structures a Data Engineer Must Know

In this article we will explain what index data structures are and introduce you to some popular structures.

In today’s world, ever-increasing amounts of data are being processed. The data can be used to derive business strategies in a commercial context, but also to gain valuable information about all scientific disciplines. The data obtained must be saved, ideally as raw data, and stored for future analysis.

At the time of creation, it is not yet possible to estimate what information might be valuable at some point. So any reduction in data ultimately represents a loss. Huge amounts of data accumulate every second, and managing them is an immense task for today’s hardware and software. Mathematical tricks have to be used to optimize search mechanisms and storage functions.

Index data structures allow you to access searched data in a large data collection immensely faster. Instead of executing a search query sequentially, a so-called index data structure is used to search for a specific data record in this data set based on a search criterion.

What are Index Data Structures in Databases?

You have probably heard about indexing in connection with databases. Here, too, an index structure is formed, independent of the data structure, which accelerates the search for certain fields. This structure consists of references, which define an order relation to the table columns. Based on these pointers, the database management system can then find the data using a search algorithm.

schematic representation of index data structures in databases
Index Data Structures in databases

However, indexing is a very complex scientific field. Queries are constantly being made more efficient and optimized. Thus, the approaches are diverse and very mathematical. This article will give you an overview of popular index data structures and help you to optimize your data pipelines.

Index data structure types

There are many different indexing methods. They are all based on different mathematical assumptions. You should understand these assumptions and choose a suitable system according to your data properties.
In the following scheme you can see some structure types you have to distinguish between, depending on the data you want to index.

index structures 1
index data structure types

The most important distinction, however, is whether you want to index one-dimensional or multidimensional data relationships. This means that you have to differentiate whether there is a common feature or several related but independent features.
In the following figure, we have classified the individual index structures according to their dimension coverage.

we have classified the individual index structures according to their dimension coverage.
individual index structures according to their dimension coverage

Which index structure you ultimately choose depends on many factors and should be weighed up well in advance, especially with large data sets.

Popular index data structures you should know

In the following, we will introduce you to some of the most popular indexing methods in detail. Because here, too, the key to success lies in understanding your tools and using them correctly at the right moment.

What is Hashing?

If you want to search for a value in an unsorted array, a linear search method is not optimal and too time consuming.
With the so called hashing method a hash value is used for unique object identification. This is calculated by a hash function from the key and determines the storage location in an array of indices, the so-called hash table. This means that you use this function to generate a unique storage location in the table using a key.
In the following figure the hash function flow is shown again.

schematic representation of the hash function sequence in detail
hash function sequence

Important basic assumptions are, however, that the function always returns a number for an object, two identical objects always have the same number and two unequal objects do not always have different numbers.

What is a Binary tree?

A so-called binary tree is a data structure in which each element, also called node, has a maximum of two successors. The addresses of the subordinate nodes are kept track of by pointers. It is often used when data is to be stored in RAM.

What is a B-tree?

The B-tree is often used in databases and file systems, i.e. for storage on the hard disk. The tree is sorted and completely balanced. The data is stored sorted by keys. The keys are stored in its internal nodes, but need not be stored in the records at the leaves. CRUD functions run in amortized logarithmic time.


The B-tree is classified into different types according to its properties.
In the B+ tree, only copies of the keys are stored in the internal nodes. The keys are stored with the data in the leaves. To speed up sequential access, these also contain pointers to the next leaf node and are thus concatenated.
In the following scheme you see a basic B+ tree structure.

Basic representation of a b+ tree and its components
Basic b+ tree structure

The B* tree is an index structure where non-root nodes must be at least 2/3 filled. This is achieved by a modified split strategy.
In addition to indexing, partitioning also offers you the possibility of strongly optimizing the data search within a database. In this article we introduce you to this technique.

What is a SkipList?

The SkipList resembles in its structure a linked list consisting of containers, which contain the data with a unique key and a pointer to the following container. In a SkipList, however, the containers have different heights and can contain pointers to containers that do not follow directly. The idea is to speed up the search by additional pointers.

schematic representation of an index structure of the SkipList
Schematic representation of a SkipList

Calculation of the container height

All nodes have pointers on different levels. Keys can be skipped with it. The height of the list elements is calculated either regularly, or unbalanced according to mathematical rules. The search is however dependent on the list emergence or evenly randomly over the list.