EXPERT KNOWLEDGE AT A GLANCE

Category: Framework (Page 1 of 2)

NumPy vs Pandas – These are the individual strengths

NumPy vs Pandas – Since in our time in every science and economic branch ever larger amounts of data accumulate, which must be analyzed and managed performantly, the learning of a programming language has become interdisciplinary indispensable.

For many, Python is the first programming language in the classical sense, due to its beginner friendliness and mathematical focus. Python offers the possibility of accessing ready-made, optimized computational tools through the modular implementation of powerful mathematical libraries.

NumPy vs Pandas - The schme shows popular python libraries and their place in the Python ecosystem
NumPy vs Pandas – Their place in the Python ecosystem

However, this offer can also quickly become overwhelming. Which library, which framework is suitable for my purposes? Will I save myself work with this tool, or will I reach its limits? Here you can learn more about SciPy and why you should definitely prefer it over MATLAB and here we compared the two Python visualization methods matplotlib and seaborn. These Python libraries are absolutely compatible with each other and together they make a very interesting data science tool. NumPy and Pandas are perhaps two of the best-known python libraries. But what are the differences between them? We will get to the bottom of this question in this article.

What actually is NumPy?

NumPy stands for “Numerical Python” and is an open source Python library for array-based calculations. It was first released in 1995 as Numeric, making it the first implementation of a Python matrix package, and rereleased as NumPy in 2006. This library is intended to allow easy handling of vectors, matrices, or large multidimensional arrays in general.

 

The scheme shows NumPys major applications
NumPy vs Pandas – Numpys Major Applications

For performance purposes, it is written in C, a deep, machine-oriented programming language. NumPy is compatible with a wide variety of Python libraries, some of which are also based on NumPy, adding further useful functions to its power, such as: Minimization, Regression, Fourier Transform

Python and Science

As mentioned earlier, Python is the programming language most intensively used in the application domain of scientific research across all disciplines for data processing and analysis. What is very interesting here is that the solution approaches are similar across disciplines at the data level. Thus, an exchange of ideas has become indispensable and leads more and more to a fusion of the sciences.

This is only mentioned in passing, but should also emphasize the importance of this programming language and its libraries, which are so often open source and further developed by a community.

NumPy vs Pandas - The schema shows Scientific Computing with NumPy over science disciplines
NumPy vs Pandas – Scientific Computing with NumPy

NumPy was developed specifically for scientific calculations and forms the basis for many specific frameworks and libraries.

The elementary NumPy data structure

The core functionality of NumPy is based on the “ndarray” data structure.

The schema shows NumPys fundamental data structure
NumPy vs Pandas – NumPys fundamental data structure

Such an array can only hold elements of the same data type and always consists of a pointer to a contiguous memory area together with the metadata describing the data stored in it. This allows processes to access them very efficiently and manipulate them as desired.

The schema shows how NumPys fundamental data structure could be manipulate
NumPy vs Pandas – NumPys data structure is manipulable

Thus, the shape can be changed via so-called reshaping, smaller subarrays can be created within a given larger array, arrays can be split, or merged.

What is Pandas?

Pandas is an open source library for data analysis and manipulation in Python. Already released in 2008 by Wes McKinney and written in Python, Cython and in C. Pandas are used in almost all areas and find worldwide appeal in all industries.

The schema shows Pandas major applications
NumPy vs Pandas – Pandas Major Applications

The name Pandas is derived from Panel Data.
Its strength lies in the processing and analysis of tabular data and time series.

The schema shows Pandas major features
NumPy vs Pandas – Pandas Features

Especially in the pre-processing of data, pandas offers a lot of operations. In addition to high-performance filter functions, very large data volumes with over 500 thousand rows can be transformed, manipulated, aggregated and cleaned.

Pandas fundamental data structures

As a basis for the individual functions and tools that Pandas provides, the library defines its own data objects. These objects can be one, two, or even three-dimensional.

The one-dimensional series object can take up different data types in contrast to NumPys ndarrays and corresponds to a data structure with two arrays. One array as index and one array holding the actual data.

The two-dimensional DataFrame object contains an ordered collection of columns. Here, each column can consist of different data types and each value is unique by a row index and a column index.
The eponymous Panel object is then a three-dimensional dataset consisting of dataframes. These objects can be divided into major axes, which are the index rows of each DataFrame, and minor axes, which are the columns of each of the DataFrames.

NumPy vs Pandas – Conclusion

Both libraries have their similarities, which are due to the fact that Pandas is based on NumPy, but is it an either or question? No, clearly not. Pandas is based on NumPy, but adds so many individual features to its functionality that there is a clear justification for their parallel existence. They simply serve different purposes and should be used for both.


One of the main differences between the two open source libraries is the data structure used. Pandas allows analysis and manipulation on a tabular form while NumPy works mainly with numerical data in arrays whose objects can have up to n dimensions. These data forms are easily convertible among themselves via an interface.

Pandas is more performant especially with very large data sets (500K rows and more). This makes data preprocessing and reading from external data sources easier to perform with Pandas and can then be transferred as a NumPy array into complex machine learning or deep learning algorithms. If you want to know more about machine learning methods and their fields of application, take a look at this article from us.

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.

IaaS vs PaaS vs SaaS – The Various Facets of Cloud Computing

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.

Matplotlib vs Seaborn – Who owns the Python visualization throne?

Matplotlib vs Seaborn – Matplotlib is often the first choice when it comes to creating mathematical plots with Python. But is it always the best choice? With Seaborn there is a potent competitor.

Matplotlib 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.
Matplotlib vs Seaborn – 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.

Matplotlib vs Seaborn - This figure shows Matplotlib features sorted by their use cases.
Matplotlib vs Seaborn – 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.
Matplotlib vs Seaborn – 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.

Matplotlib also has weaknesses

Matplotlib is not perfect despite the wide feature set. For example, only poor default options for the size and colors of plots are offered. Matplotlib is often considered to be a low-level technology compared to today’s requirements. Thus, very specialized code is needed to generate appealing plots.

What is Seaborn?

Seaborn is a Python visualization library, but based on Matplotlib. This library provides a high-level interface for visualization of statistical data and not only has its own graphics library, but internally uses Matplotlib’s functionalities and data structures.
It thus offers a variety of additional features besides the śtandard Matplotlib functions.

This scheme shows the main features of Seaborn
Matplotlib vs Seaborn – Main features of Seaborn

Among other things, Seaborn provides built-in themes for designing matplotlib graphs and a dataset-oriented API for determining the relationship between variables. It can visualize both univariate and bivariate data and plot statistical time series. Estimation and plotting of linear regression models run automatically and Seaborn, unlike Matplotlib, offers optimization when processing NumPy and Pandas data structures.

So what should you choose?

Especially when it comes to deep statistics, Seaborn clearly has the edge. Matplotlib, however, is often the leaner solution due to its simplicity. So both have their strengths and weaknesses. Which tool you ultimately choose depends on the situation. You can’t do much wrong. With one solution, however, you have more contextual options. But now that you know the differences between the two, this decision will be easier for you.

ksqlDB – Efficient real-time stream transformation of data within Kafka’s data pipelines

ksqlDB vs Kafka streams – Data streams are all the rage right now. A technique to move and process huge amounts of data simultaneously without caching it.

What is Apache Kafka?

With the messagebroker Kafka, the data can be stored resource-efficiently in so-called topics as so-called logs. These topics can then be subscribed to and rewritten by any number of clients, primarily microservices.
The metadata information is stored externally in a schemaregistry and assigned to the data again via an ID when it is read. In this way, each microservice can be developed independently of technology and programming languages. The data structure remains the same.

However, if a microservice wants to access the data streams from two or more topics and these arrive with different frequencies, then the correct allocation of the data is often difficult. The so-called data stream position can be controlled with event streaming databases.

What is ksqlDB?

Especially for Apache Kafka, ksqlDB allows easy transformation of data within Kafka’s data pipelines.

The following figure shows how a software architecture with Apache Kafka and ksqlDB could look like. It is still possible to subscribe to the data streams from the messagebroker, or indirectly via ksqlDB using pulls and pushs. The communication between table and kafka is done directly via the eventstreaming platform Confluent.

The figure shows how a software architecture with Apache Kafka and ksqlDB could look like.
software architecture with Apache Kafka and ksqlDB

It can be used to materialize views asynchronously using interactive SQL queries.
So with this, microservices can enrich the data and transform it in real time.
This enables anomaly detection, real-time monitoring, and real-time data format conversion.

Event Streaming

ksqlDB is an event streaming database. Thus, it is based on continuous streams of structured event data that can be published to multiple applications in real time. The following figure shows such an event stream schematically.

ksqlDB vs Kafka streams- The figure shows such an event stream schematically.
event stream

Each individual record always consists of an event and a unique key for identification.
These event streams can be combined with streaming analytics and is a way to offload work to back-end processing applications. If you want to know more about messaging patterns and how a message is transmitted between sender and receiver, read our article.

Window-based Query Processing

ksqlDB allows continuous stream queries. These are based on window-based aggregation of events.

Windows are polling intervals that are continuously executed over the data streams. These windows can be expanded and moved as needed to handle new incoming data items.
Several window types are shown in the figure below. They differ in their composition to each other.

ksqlDB - Several window types are shown in the figure. They differ in their composition to each other.
window types

The “Tumbling” type repeats a non-overlapping interval, while the “Bouncing” type allows overlaps. In a “Session” the elements are grouped by activity sessions without allowing overlaps. The session is terminated when no elements are received for a certain time.

ksqlDB Features

In addition to continuous queries through window-based aggregation of events, ksqlDB offers many other features that are helpful in dealing with streams. For example, the last value of a column can be tracked when aggregating events from a stream into a table.


Multiple streams can be merged by real-time joins or transformed in real-time. In doing so, the database is Distributed, Fault Tolerant and Scalable.
The Kafka Connect connectors can be executed and controlled directly.
Push and pull queries are applicable to the flows. Thus, subscribers get the constantly updated results of a query, or can retrieve data in request/response flows at a specific time.

Conclusion

With Confluent’s event streaming database ksqlDB, a service is provided that offers an absolutely compatible solution for real-time data stream processing with Kafka. Kafka in particular lends itself as a central element in a microservice-based software architecture. Microservices run as separate processes and consume in parallel from the message broker. Aligning these processes remains a challenge. However, ksqlDB ensures real-time stream processing within the services.

scikit-learn – Machine learning, Data Mining and Data Analysis in Python for free

In almost no scientific discipline you can get around the programming language Python nowadays.
With it, powerful algorithms can be applied to large amounts of data in a performant way.
Open source libraries and frameworks enable the simple implementation of mathematical methods and data transports.

What is scikit-learn?

One of the most popular Python libraries is scikit-learn. It can be used to implement both supervised and unsupervised machine learning algorithms. scikit-learn primarily offers ready-made solutions for data mining, preprocessing and data analysis.
The library is based on the SciPy Toolkit (SciKit) and makes extensive use of NumPy for high performance linear algebra and array operations. If you don’t know what NumPy is, check out our article on the popular Python library.
The library was first released in 2007 and since then it is constantly extended and optimized by a very active community.
The library was written primarily in Python and is based on Cython only for some high-level operations.
This makes the library easy to integrate into Python applications.

scikit-learn Features

Easily implement many machine learning algorithms with scikit-learn. Both supervised and unsupervised machine learning are supported. If you don’t know what the difference is between the two machine learning categories, check out this article from us on the topic.
The figure below lists all the algorithms provided by the library.

The figure  lists all the upervised and unsupervised machine learning algorithms provided by scikit-learn..
machine learning algorithms provided by scikit-learn..

scikit-learn thus offers rich capabilities to recognize patterns and data relationships in a dataset. Thus, high dimensions can be reduced to visualize the relationships without sacrificing much information.
Features can be extracted and data clustering algorithms can be easily created.

Dependencies

scikit-learn is powerful and versatile. However, the library does not exist completely solitary. Besides the obvious dependency on Python, the library requires the import of other libraries for special operations.

NumPy allows easy handling of vectors, matrices or generally large multidimensional arrays. SciPy complements these functions with useful features like minimization, regression or the Fourier transform. With joblib Python functions can be built as lightweighted pipeline jobs and with threadpoolctl methods can be coordinated as threads to save resources.

SciPy turns Python into an ingenious free MATLAB alternative

Python vs MATLAB

== Open source Python library
– a collection of mathematical algorithms and convenience functions

– is mainly used by scientists, analysts and engineers for scientific computing, visualization and related activities

– Initial Realease: 2006; Stable Release: 2020
– depends on the NumPy module
→ basic data structure used by SciPy is a N-dimensional array provided by NumPy

Benefits

scipy benefits

Features

– SciPy library provides many user-friendly and efficient numerical routines:

scipy subpackages

Available sub-packages

SciPy ecosystem

– scienitific computing in Python builds upon a small core of open-source software for mathematics, science and engineering

scipy ecosystem
SciPy Core Software

More relevant Packages

– the SciPy ecosystem includes, based on the core properties, other specialized tools

scipy eco sidepackages

The product and further information can be found here:

https://www.scipy.org/

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.

Apache Flink

Overview

– Open source stream processor framework developed by the Apache Software Foundation (2016)
– Data streams with high data volume can be processed and analyzed with low delay and high speed

flink analytics
Flink provides various tools for efficient real-time processing of continuous data streams and batch data

Core functions

– diverse, specialized APIs:
→ DataStream API (Stream Processing)
→ ProcessFunctions (control of states and time; event states can be saved and timers can be added for future calculations)
→ Table API
→ SQL API
→ provides a rich set of connectors to various storage systems such as Kafka, Kinesis, Kubernetes, YARN, HDFS, Elasticsearch, and JDBC database systems
→ REST API

Stream Processing

pexels pixabay 2438
How to handle this flood of data?

== Data is processed continuously with a short delay
→ without intermediate storage of the data in separate databases
– several data streams can be processed in parallel
– Each stream can be used to derive own follow-up actions and analyses

Architecture

Data can be processed as unbounded or bounded streams:

  • Unbounded stream

    • have a start but no defined end

    • must be continuously processed

  • Bounded stream

    • have a defined start and end

    • can be processed by ingesting all data before performing any computations(== batch processing)

– Flink automatically identifies the required resources based on the application’s configured parallelism and requests them from the resource manager.

In case of a failure, Flink replaces the failed container by requesting new resources.

– Stateful Flink applications are optimized for local state access





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