EXPERT KNOWLEDGE AT A GLANCE

Category: platform (Page 1 of 2)

Is Hadoop dead? Should I invest time to learn the Hadoop ecosystem?

Is Hadoop dead – In the IT sector in particular, technologies and software architectures do not have a long shelf life. As new technical insights are gained, the requirements and use cases for the systems also change. As young as the term “big data” is, it is also undergoing constant change. The increased acceptance of open source projects in the business community has led to increased diversification and thus to many mutually beneficial competitive situations.
Apache Hadoop has been considered the one all-purpose solution for over a decade. A Big data ecosystem in which Hadoop plays together with many other extensions. In recent years, however, more and more people are claiming that the demands on data processing have changed and see Hadoop as an outdated concept.

A few years ago, the primary goal was to efficiently handle ever-increasing data volumes, but today iterative real-time analyses on dynamic data sets are required. Data management systems must not be self-contained, but must remain manipulable and monitorable at all times.
So is Hadoop dead, or still indispensable?

What is Hadoop?

Hadoop is a Linux-based open source Big Data framework for scalable, distributed software. It is originally based on Google’s MapReduce algorithm and enables computationally intensive processes of large data sets by parallelizing them on computer clusters, i.e. a large number of networked computers, using multiple components working together.

Is Hadoop dead? This diagram shows the Hadoop ecosystem
Is Hadoop dead? Hadoop ecosystem

The Hadoop ecosystem is composed of the Hadoop Common, an interface for all other components. It connects Hadoop to the file system of the computers and contains the libraries.In the Hadoop Distributed File System
( HDFS ) very large amounts of data are stored. This is organized as a server cluster with master and slave nodes. The resources are controlled via the Yet Another Resource Negotiator (YARN) component. This resource manager distributes the individual tasks to the available resources, such as CPU and memory.

What is the MapReduce algorithm?

Google’s MapReduce programming model, even though it is currently being replaced by engines based on Directred-Acyclic-Graph (DAG), is still a core component of the Hadoop framework. So if we want to understand how Hadoop works, we first need to understand what MapReduce is in the first place.

Is Hadoop dead? This diagram shows the principle behind Google's MapReduce algorithm
Is Hadoop dead? Googles Map Reduce Algorithm principle

Configurable classes for Map, Reduce and Combination phases are provided via the Hadoop MapReduce framework. Map means that a set of data is transformed into another set of data, where the individual elements of the data are combined into tuples (key/value pairs). In the Reduce phase, the formed tuples are then combined into smaller sets of tuples.

How a Hadoop cluster works

As mentioned earlier, Hadoop distributes storage and processing of large amounts of data in a balanced manner across compute clusters, or interconnected hardware.
These computers are connected to a dedicated server that acts as the master
components. The master node organizes the storage of files and the metadata in the individual slave nodes. Within a cluster, data is stored on multiple computers called nodes. The files are partitioned into data blocks and distributed redundantly among the nodes.

Is Hadoop dead? This diagram shows the components of a Hadoop cluster
Is Hadoop dead? Components of a Hadoop Cluster

The NameNode and Resource Manager run on the master node. These collect data in the Hadoop Distributed File System (HDFS) and store data with parallel computations by applying MapReduce.

The client nodes are responsible for loading the data into the cluster’s
Architecture. The slave node is one responsible for collecting the data
Client nodes.

How does communication within a cluster work?

The internal communication, i.e. the process of job execution, is organized via so-called JobTrackers and TaskTrackers.
The client submits a MapReduce job to the JobTracker on the master to process a particular file.The JobTracker then determines the DataNodes that store the blocks for that file by querying the NameNode. The NameNode manages the HDFS file system metadata, so it keeps track of all the files that are divided into blocks. The DataNodes store and retrieve these blocks. Then tasks are assigned to different TaskTrackers based on the information received from the NameNode . In the process, the status of each task NameNode and DataNode is monitored.
A secondary NameNode communicates with the NameNode at a periodic interval to take the snapshot of the HDFS metadata. In other words, a backup. This information can then be used in the event of a NameNode failure.

Is Hadoop dead? This scheme the internal communication of the components of a Hadoop cluster
Is Hadoop dead? internal communication of the components of a Hadoop cluster

In principle, both single-node clusters and multi-node clusters can be implemented with Hadoop. In the case of a single node, the cluster is implemented on one machine only. All processes then run on a Java virtual machine instance.
In the case of multi-nodes, the master slave architecture already discussed is then implemented over several computers.

Is Hadoop dead?

So is Hadoop dead? Apache Hadoop has clearly lost its status as the sole Big Data solution. Many technologies have already been added that can solve smaller tasks better than the big one solution Hadoop.Today, this small-scale nature enables Big data management solutions that can be optimally tailored to specific use cases. However, Hadoop Hadoop is not dead either. The system still has its strengths and will continue to be the first choice for special use cases in the foreseeable future.

So how is Hadoop evolving?

With the Hadoop Ozone project, an alternative to the Hadoop Distributed File System (HDFS) has now been developed.
It is still to be deployed on a cluster, but corresponds to an object store for Big Data applications. This is much more scalable than than standard file systems and is intended to optimize the handling of small files, a previous Hadoop weakness. Object stores are typically used as a data storage method in the cloud. Through Ozone, they can now be managed locally.
This object store can be accessed by established Big Data solutions such as Hive or Spark without modification.If you want to know more about the hadoop compatible frameworks read our articles on Hive and Spark.


Ozone is built on a block storage layer called Hadoop Distributed Data Store (HDDS) and is designed to scale to billions of objects. The blocks are organized internally using unique namespaces in many independent volumes.
However, one disadvantage of these local object stores is that they are not yet implemented in the core, but must be separated from the traditional file systems by containerized environments such as Kubernetes and YARN. So there are always two truths.

Apache Hive Architecture – Data Warehouse System for free

Apache Hive Architecture – On the way to Industry 4.0, companies are trying to record all business processes as far as possible in order to subsequently optimize them through analysis.
Data warehouse systems provide central data management. Thus, only one data truth exists. In addition to persistence, these information systems take care of sorting, preprocessing, translation and data analysis.
If you want to know more about what a data warehouse system is, check out our article on the subject.

What is Apache Hive

Hive is a data warehousing software project and part of Apache, an open source and free web server software. Learn more about Apache here.
It is built on the Big Data framework Apache Hadoop and was released in 2010. Since then it has been continuously improved and extended by an industrious community.

hive
Apache Hive Architecture – Built on top of Hadoop

The query language used by Hive, called HiveQL, is SQL based and allows querying, aggregation and analysis of unstructured data. Hive does not work with the schema-on-write (SoW) approach like relational databases, but uses the so-called schema-on-read (SoR) approach.

What are the biggest advantages of Hive?

Data from relational databases is automatically converted into MapReduce or Tez or Spark jobs. Hadoopclusters are based on MapReduce, a Google programming model for concurrent computation on computer clusters, and powerful stream-based data analysis pipelines can be created with Apache Spark. This ensures full compatibility with the Apache ecosystem, which can be modularly tailored to the needs of an application.

The figure shows the main Apache Hive features
Apache Hive Features

Another advantage of Hive is that the tables are similar to the tables in a relational database. Data is queried using HiveQL. A declarative SQL-like language.
HiveQL allows multiple users to query data simultaneously. Hive supports a variety of data formats and provides a lightweight but powerful translation feature.
For data analysis, custom MapReduce processes can be written and run on clusters in parallel for high performance.

Apache Hive Architecture

Basically, the architecture of Hive can be divided into three core areas. Hive communicates with other applications via the client area. The integration is then executed via the service area. In the last layer, Hive stores the metadata, for example, or computes the data via Hadoop.

The figure shows the basic three-part core architecture of Apache Hive.
Apache Hive Architecture

Hive Clients

Apache Hive can be accessed via different clients. In addition to Open Database Connectivity (ODBC), an SQL-based application programming interface (API) created by Microsoft, there is Java Database Connectivity (JDBC), an SQL-based API developed by Sun Microsystems to allow Java applications to use SQL for database access. Hive also provides a high-performance Apache Thrift connection.

Hive Services

The core and central control of the Hive Services is the so-called driver. This
receives HiveQL commands and is responsible for their execution against the Hadoop system. It typically consists of a compiler that translates HiveQL requests into abstract syntax and executable tasks, an optimizer that aggregates, splits, and optimizes for better performance and scalability, and an executor that interacts with Hadoop’s job tracker and passes tasks to the system for execution.

Apache Hive also provides the ability to submit these tasks directly to the driver. Using the Command Line and User Interface (CLI + UI), it is possible to directly influence the process.

Metadata about persistent relational entities, i.e. databases, tables, columns and partitions are managed by the metastore.

Hive Storage and Computer

The metadata is stored here in a persistence. The results of the query and the data loaded into the tables are stored on HDFS in the Hadoop cluster.

Things you need to know when you start using Apache Spark

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 is a MES system?

MES System, PLM and ERP – These terms are being mentioned more and more often in connection with Industry 4.0. But what is behind these systems and what are the differences? In this article, we will explain what a Manufacturing Execution System (MES system) actually is and what tasks it has.

Classification MES
Relationship between SCADA, Field Level, PLC, ERP and MES

The MES system is an operational process-related part of a multi-layer MES System. It is responsible for real-time production management and control. You can use MES data to optimize manufacturing processes and detect errors during the production process. The MES system is assigned to the ERP system. This system accesses your MES data to plan production. It then feeds this information back to your production control system for implementation. You can learn more about ERP here.
Thus, the basis of your MES usually the information from the ERP and the production planning PLM.
Learn more about PLM here.

Relationship between company level
Relationship between company level


The interaction of the individual components is moving closer together in Industry 4.0. To what extent you can read here.

What does the MES include?

MES is usually a multi-layer overall system. It processes your production data into Key Performance Indicators (KPI) and enforces the fulfillment of an existing production plan.

this diagram clearly shows all components of a MES System
Overview MES System features

MES and Industry 4.0

You’ve probably heard of the term Industry 4.0. It describes the manufacturing of the future. By using software solutions, you can now analyze large data streams in real time. This allows you to optimize your individual production steps and shorten life cycles.
The MES plays an important role here due to its proximity to production. This allows you to make important decisions quickly and implement production plans.In your company of the future, software solutions from various divisions are networked with each other. So you can form information chains and the MES is part of this network.

Connection Industry 4.0 and MES System
Connection Industry 4.0 and MES System

What is a PLM system?

In addition to MES and ERP, the Product Life Cycle Management (PLM) system plays an elementary role in the digitization of your company. Learn more about software solutions in an Industry 4.0 company here.

In order for your company to remain internationally competitive in today’s world, you need to optimize your business models in order to be able to act preventively.

As a manufacturing company, you need to be able to analyze large amounts of data quickly. This way you can recognize deviations from the plan early on and make the right decisions.

Many software solutions help you in all business areas and even exchange data with each other. In this way, you can create information chains within a company and act more quickly. 

Improve your business with the integration of a PLM system

PLM System is a management approach for the seamless integration of all information that accumulates during the life cycle of a product.
The core components of PLM are the data and information related to the product lifecycle.

Product Life Cycle

Overview of the product life cycle
Product Life Cycle

You can actually define a product life cycle as a
business concept. It describes the sum of the individual corporate functions through which a product passes. This process forms a circle from planning to reuse. Here is an interesting article that explains in more detail the concept behind the product life cycle.

PLM System – Here you get an overview

Overview of all components of plm system
PLM

A large amount of product-related and time-dependent data is generated along the product life cycle. The PLM enterprise concept is based on coordinated methods, processes and organizational structures and usually makes use of IT systems. PLM tools link design, implementation and production and provide feedback from manufacturing. The goal of a PLM system is the central management of information and corresponding user groups. One advantage here is that you can control the process of editing and distribution throughout the company.

Application Lifecycle Management (ALM) vs PLM System

More and more products and systems now contain a software component. However, since hardware and software are historically different, you must also differentiate between the management systems.
With PLM you are looking at a physical product, with ALM you are looking at a software product. Basically, however, there are similarities between the two systems. Both also track a product over its entire lifecycle. However, since both product types are increasingly merging today, you can also link both systems on an IT basis at the overall product level.

Comparison of ALM and PLM
ALM vs PLM

What is ERP? A Simplified Quick-start Guide

You have certainly heard such terms as MES, PLM and ERP in connection with Industry 4.0.
In this article we will discuss the question:
What is ERP and why shouldn’t you do without it?

To stay competitive in today’s world, you need to increase the efficiency of your business processes. It is important that you optimally plan, control and manage your operational resources (capital, personnel…).

Your goal should be to create high quality and continuity with high productivity and low lead time.
Many of your business processes create ever larger amounts of data and increase in complexity. You need to reduce this complexity and increase your flexibility.
Many software solutions are available to your company for the optimal use of resources.

What is ERP System – Overview

what is erp system - this diagram clearly shows you all the components of an erp system
What is ERP – Overview ERP System

Basically, an ERP system is an IT-supported system of software solutions that communicate with each other. Your data is stored centrally and should represent your company in its entirety through quickly available information. The information of your business processes is optimized and documented.
The trend is towards web-based applications.
This means that you access the system interface via your browser. This means that you can also access it beyond the boundaries of your company. Another advantage is that you don’t have to install any services, making you hardware-independent.

What are ERP Subsystems?

what is erp system - this diagram shows you clearly in which business areas an erp system is used
in which business areas is an erp system used

You can use ERP systems in all areas of your business. They provide you with complete solutions for all necessary subsystems. Complex systems are divided into so-called
application modules, which you can combine with each other as you wish. These fulfill various tasks for the provision and further processing of information. In this way, you can put together your ERP system according to your requirements and adapt it to the size of your company.

What is Advantages Cloud ERP?

ERPs can also be purchased as a complete Software-as-a-Service (SaaS) solution.

These are completely industry and hardware independent. You, as a user, can access a sophisticated ERP software package online and thus from anywhere. This gives you absolute spatial flexibility. However, Cloud ERP solutions are still quite new and not yet fully mature. So you should weigh up well in advance whether you want to use a cloud application.

erp cloud
What is ERP – ERP Cloud Advantages

Microsoft Power Platform – Microsoft’s Web tool to automate your workflows

In this article, we will show you everything about the cloud-based web tool Microsoft Power Platform and why you shouldn’t do without it.

Cloud-based web development has gained popularity in the web development industry in recent years. The globalization of the workforce and the diversification of the work process have significantly driven the development of cloud-based services.

What is Microsoft Power Platform?

With Power Platform you get an integrated application platform consisting of a group of different Microsoft products with which you can develop complex business solutions. This way you can make your business processes more efficient and productive. The platform can also take care of data storage, entry and processing. Data analysis via complex visualizations and predictions can also be handled by various services.

Microsoft Power Platform Services

Schematic representation of the Microsoft Power Platform Services
Power Platform Services

Since the Power Platform is a collection of different Microsoft services, we want to give you an overview of the individual parts.

Build your own Apps

With Power Apps you get a user suite for mapping custom apps. All apps are independent of data sources and can be extended by you as you wish via drag & drop. This way you can adapt them to your needs.

Automate your tasks

Power Automate was still called Microsoft Flow until 2019. This web tool lets you automate recurring tasks and simple cross-platform workflows. You can connect to over 100 third-party systems via connectors. This allows you to automate processes outside the Microsoft environment and across applications.

Schematic representation of the Principle Microsoft Power Automate
Principle Microsoft Power Automate

Analyze your business data

With Power BI you get a business intelligence tool. With this you can access different data sources. An advantage to other BI tools is the deep integration with Excel. So you can create user-friendly, data connections and visualize.

Should I choose Microsoft Power Platform?

You need more and more scalable and secure solutions at low cost. Device-independent access to important planning and evaluation software will increasingly become the focus of attention and change global corporate structures. To turn your company into an Industry 4.0 enterprise, you can no longer avoid cloud solutions.

The question is not: if, but when you will choose a corresponding service. The question is not: if, but when you will choose a corresponding service. Microsoft, as one of the largest IT companies, now presents you with a solution. Whether it fits your needs, however, you must ultimately decide for yourself.

IaaS vs PaaS vs SaaS – What are the differences?

IaaS vs PaaS vs SaaS – terms that categorize clouds, but what exactly do they mean? In this article, we contrast all three and explain the differences.

In almost all areas, the cloud is becoming more and more important. Increasingly, the cloud is also becoming interesting for business processes. Everyone is talking about it, but what is it actually?

What is the cloud anyway?

The cloud basically means the use of different servers. This means that your data can be hosted online, i.e. stored, managed and processed.
So you don’t have to provide the appropriate hardware on site, but can rent these resources from a cloud provider. Read our article about the cloud computing provider AWS.
Besides Amazon, other global players such as Google (Google Cloud) and Microsoft (Azure) also offer profitable cloud resources.
But which ones are suitable for me or my company? To meaningfully compare the individual solutions, you need to understand the differences between them.
Basically, you need to distinguish between the three categories already mentioned.

IaaS vs PaaS vs SaaS - Diese Abbildung zeigt die Die 3 Cloud Kategorien
IaaS vs PaaS vs SaaS

IaaS vs PaaS vs SaaS – What are the Differences?

First and foremost, all three terms are used to describe a resource provided by a cloud service provider for a short period of time.
The following figure shows this “as-a-service”, or Flexible consumption model, and the management components..

IaaS vs PaaS vs SaaS - This diagram shows the distribution of tasks between providers and customers in the individual cloud categories depending on the service layer model.
Red: managed by others; Green: managed by your organization

You can see very clearly here that the cloud provider manages more and more layers, ascending from IaaS to SaaS.

Software as a Service (SaaS)

The abbreviation SaaS refers to cloud-based software. This is hosted online by a company and provided via the Internet. It is easy to use and manage. Additionally, it is highly scalable, meaning it can be used for an entire organization.

Platform as a Service (PaaS)

PaaS is used to describe a cloud-based platform service. This offers developers an online platform for application development. Data is provided, stored and managed online.s

Infrastructure as a Service (IaaS)

IaaS refers to cloud-based infrastructure resources provided via virtualization technologies. These services are designed to help companies build and manage their servers, networks, operating systems and data storage. This is where the highest administrative share lies with the customer. Access to the servers for data management takes place via a dashboard or API.

IaaS vs PaaS vs SaaS – For whom is which category suitable?

So who should choose which service model? The following figure shows that the more tasks are taken over by the provider, the more control is relinquished. This is especially detrimental in organizations where a lot of control is needed.

IaaS vs PaaS vs SaaS - Presentation of the individual services depending on the control and for whom they are suitable.
Services depending on the control

IaaS gives administrators more direct needed, control over operating systems. However, more control always comes with more complicated administration tasks. PaaS therefore offers users a certain compromise between flexibility and ease of use. This model is particularly appealing to developers.
The SaaS model offers the highest level of usability and is accordingly interesting for customers who want to take over no to few administrative tasks.

IaaS vs PaaS vs SaaS – Technology of the future?

Cloud resources can be a valuable alternative to expensive, in-house hardware solutions. Of course, with external administration, a company loses control over its own data. However, the different types of service mean that compromises can be made that are tailored to the company’s own needs.

The advantages are obvious. Individual services can be accessed from virtually anywhere at any time, and high-performance computing can be operated cost-effectively. As network technologies become faster and faster, these solutions are increasingly coming into focus and will certainly become more and more important for companies and private individuals in the coming years.

Python Matplotlib – Free Powerful Plotting Library

Python Matplotlib – This library is the first choice when it comes to creating mathematical plots. It was developed by John D. Hunter back in 2003 and has become indispensable. Due to the increasing importance of the Python programming language in almost all scientific areas, the importance of fully compatible visualization methods is also growing.


Due to its open source concept, Matplotlib can be used absolutely free of charge and is a basic component of many popular Python distribution platforms, such as Anaconda.


The library offers a MATLAB-like interface and can be used in combination with NumPy, Pandas and Scipy, just like MATLAB.

SciPy is a collection of mathematical algorithms and convenience functions and is mainly used by scientists, analysts and engineers for scientific computing, visualization and related activities.
NumPy allows easy handling of vectors, matrices, or large multidimensional arrays in general.
NumPy’s operators and functions are optimized for multidimensional array operations and evaluate particularly efficiently.

Pandas is also an open source Python library that can be used to perform data analysis and manipulation efficiently. Its strength lies in the processing and evaluation of tabular data and time series.

These components, which are absolutely compatible with each other, offer in their entirety an absolutely free, but fully comprehensive alternative to the commercial analysis software MATLAB.

This figure shows some Python libraries, which together form an open source MATLAB alternative.
Python Matplotlib – Together the Python libraries form a MATLAB replacement

Python Matplotlib – What are the features?

The library offers a wide range of visualization functions. Some of them are listed in the figure below.

Python Matplotlib - This figure shows Matplotlib features sorted by their use cases.
Python Matplotlib – Matplotlib Features

Matplotlib is designed to effectively visualize the results of mathematical calculations. Visualization is an efficient and important data analysis tool.
The library is able to generate all the usual diagrams and figures by default. It is even possible to create animations that can be used to better understand the flow of certain algorithms.

Event Handling

Matplotlib offers an important feature with event handling. Behind the name is a UI-neutral event model. This allows the library to connect to events without knowing which UI Matplotlib will eventually plug into.


This allows me to develop a very flexible and portable code.
However, the events can then be used to transfer things like the data coordinate.

PyLab vs Pyplot

PyLab is a collection of functions that is installed together with Matplotlib and make the library work like MATLAB.
The module brings a set of NumPy functions and classes into the namespace. This makes them accessible without having to import them.
However, this often led to conflicts between individual Matplotlib functions.
For this reason, the use of PyLab is now no longer recommended.
Pyplot is a module in Matplotlib and provides the state-machine interface to the underlying plotting library.


The conflicts are prevented because an import is done with Pyplot and a separate NumPy import.

Python Matplotlib – Third party packages

If the standard library features are not enough, you can extend Matplotlib with additional external packages. In the following figure some of the possible extensions are listed and grouped by application.

Python Matplotlib - This figure shows Matplotlib Third Party Packages sorted by their use cases.
Python Matplotlib – Third Party Packages

These external packages must be installed individually and extend the functionality of the plotting library, or build on existing features.
They sometimes offer more complex graphics or higher performance data analysis methods. Most of these packages are open source and are constantly updated by very active communities. In this article we have compiled some information about the popular extension Seaborn. The module extends the Matplotlib visualizations with valuable special plots.

Apache Mahout – A Powerful Open Source Machine Learning Project

Apache Mahout is a powerful machine learning tool that comes with a seamless compatibility to the strong big data management frameworks from the Apache universe. In this article, we will explain the functionalities and show you the possibilities that the Apache environment offers.

What is Machine Learning?

Machine learning algorithms provide lots of tools for analyzing large unknown data sets.
The art of data science is to extract the maximum amount of information depending on the data set by using the right method. Are there patterns in the high-dimensional data relationships, and how can they be represented in a low-dimensional way without much loss of information?

scikitLearn ml
Fields of machine learning


There is often a similar amount of information in the failure as when an algorithm was able to successfully create groupings.
It is important to understand the mathematical approaches behind the tools in order to draw conclusions about why an algorithm did not work.
If you don’t know the basic machine learning categories, it’s best to read our article on the subject first.

Machine Learning and Linear Algebra

Most machine learning methods are based on linear algebra.
This mathematical subfield deals with linear transformations, vector spaces and linear mappings between them.
The knowledge of the regularities is the key to the correct understanding of machine learning algorithms.

What is Apache Mahout

Apache Mahout is an open source machine learning project that builds implementations of scalable machine learning algorithms with a focus on linear algebra. If you’re not sure what Apache is, check out this article. Here we introduce you to the project and its main projects once.


Mahout was already released in 2009 and since then it is constantly extended and kept up-to-date by a very active community.
Originally, it contained scalable algorithms closely related to Apache Hadoop and MapReduce.
However, Mahout has since evolved into a backend independent environment. That is, it operates on non-Hadoop clusters or single nodes.

Features

The math library is based on Scala and provides an R-like Domain Specific Language (DSL). Mahout is usable for Big Data applications and statistical computing. The figure below lists all machine learning algorithms currently offered by Mahout.

The figure below lists all machine learning algorithms currently offered by Apache Mahout.
Implemented mathematical functions and algorithms

The algorithms are scalable and cover both supervised and unsupervised machine learning methods, such as clustering algorithms.

Apache Mahout covers a large part of the usual machine learning tools. This means that data can be analyzed without having to change frameworks. This is a big plus for maintaining compatibility in the application.

Apache Ecosystem

The framework integrates seamlessly into the Apache Ecosystem. This means that an application can access the entire power of the data processing platforms and build very high-performance big data pipelines. The following figure shows the Apache data management ecosystem.

Apache Mahout ecosystem
Apache Mahout ecosystem

Through connectivity to Apache Flink, stream data analysis pipelines can be built, or with Hive data from relational databases can be automatically converted into MapReduce or Tez or Spark jobs.

« Older posts