Posts

Data Science & Big Data

Buzzword Bingo: Data Science – Teil II

Im ersten Teil unserer Serie „Buzzword Bingo: Data Science“ widmeten wir uns den Begriffen Künstliche Intelligenz, Algorithmen und Maschinelles Lernen. Nun geht es hier im zweiten Teil weiter mit der Begriffsklärung dreier weiterer Begriffe aus dem Data Science-Umfeld.

Buzzword Bingo: Data Science – Teil II: Big Data, Predictive Analytics & Internet of Things

Im zweiten Teil unserer dreiteiligen Reihe „Buzzword Bingo Data Science“ beschäftigen wir uns mit den Begriffen „Big Data“, „Predictive Analytics“ und „Internet of Things“.

Big Data

Interaktionen auf Internetseiten und in Webshops, Likes, Shares und Kommentare in Social Media, Nutzungsdaten aus Streamingdiensten wie Netflix und Spotify, von mobilen Endgeräten wie Smartphones oder Fitnesstrackern aufgezeichnete Bewegungsdate oder Zahlungsaktivitäten mit der Kreditkarte: Wir alle produzieren in unserem Leben alltäglich immense Datenmengen.

Im Zusammenhang mit künstlicher Intelligenz wird dabei häufig von „Big Data“ gesprochen. Und weil es in der öffentlichen Diskussion um Daten häufig um personenbezogene Daten geht, ist der Begriff Big Data oft eher negativ konnotiert. Dabei ist Big Data eigentlich ein völlig wertfreier Begriff. Im Wesentlichen müssen drei Faktoren erfüllt werden, damit Daten als „big“ gelten. Da die drei Fachbegriffe im Englischen alle mit einem „V“ beginnen, wird häufig auch von den drei V der Big Data gesprochen.

Doch welche Eigenschaften sind dies?

  • Volume (Datenmenge): Unter Big Data werden Daten(-mengen) verstanden, die zu groß sind, um sie mit klassischen Methoden zu bearbeiten, weil beispielsweise ein einzelner Computer nicht in der Läge wäre, diese Datenmenge zu verarbeiten.
  • Velocity (Geschwindigkeit der Datenerfassung und -verarbeitung): Unter Big Data werden Daten(-mengen) verstanden, die in einer sehr hohen Geschwindigkeit generiert werden und dementsprechend auch in einer hohen Geschwindigkeit ausgewertet und weiterverarbeitet werden müssen, um Aktualität zu gewährleisten.
  • Variety (Datenkomplexität oder Datenvielfalt): Unter Big Data werden Daten(-mengen) verstanden, die so komplex sind, dass auf den ersten Blick keine Zusammenhänge erkennbar sind. Diese Zusammenhänge können erst mit speziellen maschinellen Lernverfahren aufgedeckt werden. Dazu gehört auch, dass ein Großteil aller Daten in unstrukturierten Formaten wie Texten, Bildern oder Videos abgespeichert ist.

Häufig werden neben diesen drei V auch weitere Faktoren aufgezählt, welche Big Data definieren. Dazu gehören Variability (Schwankungen, d.h. die Bedeutung von Daten kann sich verändern), Veracity (Wahrhaftigkeit, d.h. Big Data muss gründlich auf die Korrektheit der Daten geprüft werden), Visualization (Visualisierungen helfen, um komplexe Zusammenhänge in großen Datensets aufzudecken) und Value (Wert, d.h. die Auswertung von Big Data sollte immer mit einem unternehmerischen Vorteil einhergehen).

Predictive Analytics

  • Heute schon die Verkaufszahlen von morgen kennen, sodass eine rechtzeitige Nachbestellung knapper Produkte möglich ist?
  • Bereits am Donnerstagabend die Regenwahrscheinlichkeit für das kommende Wochenende kennen, sodass passende Kleidung für den Kurztrip gepackt werden kann?
  • Frühzeitig vor bevorstehenden Maschinenausfällen gewarnt werden, sodass die passenden Ersatzteile bestellt und das benötigte technische Personal angefragt werden kann?

Als Königsdisziplin der Data Science gilt für viele die genaue Vorhersage zukünftiger Zustände oder Ereignisse. Im Englischen wird dann von „Predictive Analytics“ gesprochen. Diese Methoden werden in vielen verschiedenen Branchen und Anwendungsfeldern genutzt. Die Prognose von Absatzzahlen, die Wettervorhersage oder Predictive Maintenance (engl. für vorausschauende Wartung) von Maschinen und Anlagen sind nur drei mögliche Beispiele.

Zu beachten ist allerdings, dass Predictive-Analytics-Modelle keine Wahrsagerei sind. Die Vorhersage zukünftiger Ereignisse beruht immer auf historischen Daten. Das bedeutet, dass maschinelle Modelle mit Methoden des überwachten maschinellen Lernens darauf trainiert werden, Zusammenhänge zwischen vielen verschiedenen Eingangseigenschaften und einer vorherzusagenden Ausgangseigenschaft zu erkennen. Im Falle der Predicitve Maintenance könnten solche Eingangseigenschaften beispielsweise das Alter einer Produktionsmaschine, der Zeitraum seit der letzten Wartung, die Umgebungstemperatur, die Produktionsgeschwindigkeit und viele weitere sein. In den historischen Daten könnte ein Algorithmus nun untersuchen, ob diese Eingangseigenschaften einen Zusammenhang damit aufweisen, ob die Maschine innerhalb der kommenden 7 Tage ausfallen wird. Hierfür muss zunächst eine ausreichend große Menge an Daten zur Verfügung stehen. Wenn ein vorherzusagendes Ereignis in der Vergangenheit nur sehr selten aufgetreten ist, dann stehen auch nur wenige Daten zur Verfügung, um dasselbe Ereignis für die Zukunft vorherzusagen. Sobald der Algorithmus einen entsprechenden Zusammenhang identifiziert hat, kann dieses trainierte maschinelle Modell nun verwendet werden, um zukünftige Maschinenausfälle rechtzeitig vorherzusagen.

Natürlich müssen solche Modelle dauerhaft darauf geprüft werden, ob sie die Realität immer noch so gut abbilden, wie zu dem Zeitpunkt, zu dem sie trainiert worden sind. Wenn sich nämlich die Umweltparameter ändern, das heißt, wenn Faktoren auftreten, die zum Trainingszeitpunkt noch nicht bekannt waren, dann muss auch das maschinelle Modell neu trainiert werden. Für unser Beispiel könnte dies bedeuten, dass wenn die Maschine für die Produktion eines neuen Produktes eingesetzt wird, auch für dieses neue Produkt zunächst geprüft werden müsste, ob die in der Vergangenheit gefundenen Zusammenhänge immer noch Bestand haben.

Internet of Things

Selbstfahrende Autos, smarte Kühlschränke, Heizungssysteme und Glühbirnen, Fitnesstracker und vieles mehr: das Buzzword „Internet of Things“ (häufig als IoT abgekürzt) beschreibt den Trend, nicht nur Computer über Netzwerke miteinander zu verbinden, sondern auch verschiedene alltägliche Objekte mit in diese Netzwerke aufzunehmen. Seinen Anfang genommen hat dieser Trend in erster Linie im Bereich der Unterhaltungselektronik. In vielen Haushalten sind schon seit Jahren Fernseher, Computer, Spielekonsole und Drucker über das Heimnetzwerk miteinander verbunden und lassen sich per Smartphone bedienen.

Damit ist das IoT natürlich eng verbunden mit Big Data, denn all diese Geräte produzieren nicht nur ständig Daten, sondern sie sind auch auf Informationen sowie auf Daten von anderen Geräten angewiesen, um zu funktionieren.

5 Apache Spark Best Practices

Already familiar with the term big data, right? Despite the fact that we would all discuss Big Data, it takes a very long time before you confront it in your career. Apache Spark is a Big Data tool that aims to handle large datasets in a parallel and distributed manner. Apache Spark began as a research project at UC Berkeley’s AMPLab, a student, researcher, and faculty collaboration centered on data-intensive application domains, in 2009. 

Introduction

Spark’s aim is to create a new framework that was optimized for quick iterative processing, such as machine learning and interactive data analysis while retaining Hadoop MapReduce’s scalability and fault-tolerant. Spark outperforms Hadoop in many ways, reaching performance levels that are nearly 100 times higher in some cases. Spark has a number of components for various types of processing, all of which are based on Spark Core. Today we will be going to discuss in brief the Apache  Spark and 5 of its best practices to look forward to-

What is Apache Spark?

Apache Spark is an open-source distributed system for big data workforces. For fast analytic queries against another size of data, it uses in-memory caching and optimised query execution. It is a parallel processing framework for grouped computers to operate large-scale data analytics applications. This could handle packet and real-time data processing and predictive analysis workloads.

It claims to support code reuse all over multiple workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing—and offers development APIs in Java, Scala, Python, and R. With 365,000 meetup members in 2017, Apache Spark is becoming one of the most renowned big data distributed processing frameworks. Explore for Apache Spark Tutorial for more information.

5 best practices of Apache Spark

1. Begin with a small sample of the data.

Because we want to make big data work, we need to start with a small sample of data to see if we’re on the right track. In my project, I sampled 10% of the data and verified that the pipelines were working properly. This allowed me to use the SQL section of the Spark UI to watch the numbers grow throughout the flow while not having to wait too long for it to complete.

In my experience, if you attain your preferred runtime with a small sample, scaling up is usually simple.

2. Spark troubleshooting

For transformations, Spark seems to have a lazy loading behaviour. That is, it will not initiate the transformation computation; instead, it will keep records of the transformation requested. This makes it difficult to determine where in our code there are bugs or areas that need to be optimised. Splitting the code into sections with df.cache() and then using df.count() to force Spark to calculate the df at every section was one practise that we found useful.

Spark actions seem to be keen in that they cause the underlying action to perform a computation. So, if you’ve had a Spark action which you only call when it’s required, pay attention. A Spark action, for instance, is count() on a dataset. You can now inspect the computation of each section using the spark UI and identify any issues. It’s important to note that if you don’t use the sampling we mentioned in (1), you’ll probably end up with a very long runtime that’s difficult to debug.

Check out Apache Spark Training & Certification Course to get yourself certified in Apache Spark with industry-level skills.

3. Finding and resolving Skewness is a difficult task.

Having to look at the stage specifics in the spark UI and looking for just a major difference between both the max and median can help you find the Skewness:

Let’s begin with a definition of Skewness. As previously stated, our data is divided into partitions, and the size of each partition will most likely change as the progress of transformation. This can result in a large difference in size between partitions, indicating that our data is skew. This implies that a few of the tasks were markedly slower than the rest.

Why is this even a bad thing? Because it may cause other stages to stand in line for these few tasks, leaving cores idle. If you understand where all the Skewness has been coming from, you can fix it right away by changing the partitioning.

4. Appropriately cache

Spark allows you to cache datasets in memory. There are a variety of options to choose from:

  • Since the same operation has been computed several times in the pipeline flow, cache it.
  • To allow the required cache setting, use the persist API to enable caching (persist to disc or not; serialized or not).
  • Be cognizant of lazy loading and, if necessary, prime cache up front. Some APIs are eager, while others aren’t.
  • To see information about the datasets you’ve cached, go to the Storage tab in the Spark UI.
  • It’s a good idea to unpersist your cached datasets after you’ve finished using them to free up resources, especially if other people are using the cluster.

5. Spark has issues with iterative code.

It was particularly difficult. Spark uses lazy evaluation so that when the code is run, it only creates a computational graph, a DAG. Once you have an iterative process, however, this method can be very problematic so because DAG finally opens the prior iteration and then becomes extremely large, we mean extremely large. This may be too large for the driver to remember. Because the application is stuck, this makes it appear in the spark UI as if no jobs are running (which is correct) for an extended period of time — until the driver crashes.

This seems to be presently an obvious issue with Spark, and the workaround that worked for me was to use df.checkpoint() / df.reset() / df.reset() / df.reset() / df.reset() / df. every 5–6 iterations, call localCheckpoint() (find your number by experimenting a bit). This works because, unlike cache(), checkpoint() breaks the lineage and the DAG, saves the results and starts from a new checkpoint. The disadvantage is that you don’t have the entire DAG to recreate the df if something goes wrong.

Conclusion

Spark is now one of the most popular projects inside the Hadoop ecosystem, with many companies using it in conjunction with Hadoop to process large amounts of data. In June 2013, Spark was acknowledged into the Apache Software Foundation’s (ASF) entrepreneurial context, and in February 2014, it was designated as an Apache Top-Level Project. Spark could indeed run by itself, on Apache Mesos, or on Apache Hadoop, which is the most common. Spark is used by large enterprises working with big data applications because of its speed and ability to connect multiple types of databases and run various types of analytics applications.

Learning how to make Spark work its magic takes time, but these 5 practices will help you move your project forward and sprinkle some spark charm on your code.

Big Data mit Hadoop und Map Reduce!

Foto von delfi de la Rua auf Unsplash.

Hadoop ist ein Softwareframework, mit dem sich große Datenmengen auf verteilten Systemen schnell verarbeiten lassen. Es verfügt über Mechanismen, welche eine stabile und fehlertolerante Funktionalität sicherstellen, sodass das Tool für die Datenverarbeitung im Big Data Umfeld bestens geeignet ist. In diesen Fällen ist eine normale relationale Datenbank oft nicht ausreichend, um die unstrukturierten Datenmengen kostengünstig und effizient abzuspeichern.

Unterschiede zwischen Hadoop und einer relationalen Datenbank

Hadoop unterscheidet sich in einigen grundlegenden Eigenschaften von einer vergleichbaren relationalen Datenbank.

Eigenschaft Relationale Datenbank Hadoop
Datentypen ausschließlich strukturierte Daten alle Datentypen (strukturiert, semi-strukturiert und unstrukturiert)
Datenmenge wenig bis mittel (im Bereich von einigen GB) große Datenmengen (im Bereich von Terrabyte oder Petabyte)
Abfragesprache SQL HQL (Hive Query Language)
Schema Statisches Schema (Schema on Write) Dynamisches Schema (Schema on Read)
Kosten Lizenzkosten je nach Datenbank Kostenlos
Datenobjekte Relationale Tabellen Key-Value Pair
Skalierungstyp Vertikale Skalierung (Computer muss hardwaretechnisch besser werden) Horizontale Skalierung (mehr Computer können dazugeschaltet werden, um Last abzufangen)

Vergleich Hadoop und Relationale Datenbank

Bestandteile von Hadoop

Das Softwareframework selbst ist eine Zusammenstellung aus insgesamt vier Komponenten.

Hadoop Common ist eine Sammlung aus verschiedenen Modulen und Bibliotheken, welche die anderen Bestandteile unterstützt und deren Zusammenarbeit ermöglicht. Unter anderem sind hier die Java Archive Dateien (JAR Files) abgelegt, die zum Starten von Hadoop benötigt werden. Darüber hinaus ermöglicht die Sammlung die Bereitstellung von grundlegenden Services, wie beispielsweise das File System.

Der Map-Reduce Algorithmus geht in seinen Ursprüngen auf Google zurück und hilft komplexe Rechenaufgaben in überschaubarere Teilprozesse aufzuteilen und diese dann über mehrere Systeme zu verteilen, also horizontal zu skalieren. Dadurch verringert sich die Rechenzeit deutlich. Am Ende müssen die Ergebnisse der Teilaufgaben wieder zu seinem Gesamtresultat zusammengefügt werden.

Der Yet Another Resource Negotiator (YARN) unterstützt den Map-Reduce Algorithmus, indem er die Ressourcen innerhalb eines Computer Clusters im Auge behält und die Teilaufgaben auf die einzelnen Rechner verteilt. Darüber hinaus ordnet er den einzelnen Prozessen die Kapazitäten dafür zu.

Das Hadoop Distributed File System (HDFS) ist ein skalierbares Dateisystem zur Speicherung von Zwischen- oder Endergebnissen. Innerhalb des Clusters ist es über mehrere Rechner verteilt, um große Datenmengen schnell und effizient verarbeiten zu können. Die Idee dahinter war, dass Big Data Projekte und Datenanalysen auf großen Datenmengen beruhen. Somit sollte es ein System geben, welches die Daten auch stapelweise speichert und dadurch schnell verarbeitet. Das HDFS sorgt auch dafür, dass Duplikate von Datensätzen abgelegt werden, um den Ausfall eines Rechners verkraften zu können.

Map Reduce am Beispiel

Angenommen wir haben alle Teile der Harry Potter Romane in Hadoop PDF abgelegt und möchten nun die einzelnen Wörter zählen, die in den Büchern vorkommen. Dies ist eine klassische Aufgabe bei der uns die Aufteilung in eine Map-Funktion und eine Reduce Funktion helfen kann.

Bevor es die Möglichkeit gab, solche aufwendigen Abfragen auf ein ganzes Computer-Cluster aufzuteilen und parallel berechnen zu können, war man gezwungen, den kompletten Datensatz nacheinander zu durchlaufen. Dadurch wurde die Abfragezeit auch umso länger, umso größer der Datensatz wurde. Der einzige Weg, um die Ausführung der Funktion zu beschleunigen ist es, einen Computer mit einem leistungsfähigeren Prozessor (CPU) auszustatten, also dessen Hardware zu verbessern. Wenn man versucht, die Ausführung eines Algorithmus zu beschleunigen, indem man die Hardware des Gerätes verbessert, nennt man das vertikale Skalieren.

Mithilfe von MapReduce ist es möglich eine solche Abfrage deutlich zu beschleunigen, indem man die Aufgabe in kleinere Teilaufgaben aufsplittet. Das hat dann wiederum den Vorteil, dass die Teilaufgaben auf viele verschiedene Computer aufgeteilt und von ihnen ausgeführt werden kann. Dadurch müssen wir nicht die Hardware eines einzigen Gerätes verbessern, sondern können viele, vergleichsweise leistungsschwächere, Computer nutzen und trotzdem die Abfragezeit verringern. Ein solches Vorgehen nennt man horizontales Skalieren.

Kommen wir zurück zu unserem Beispiel: Bisher waren wir bildlich so vorgegangen, dass wir alle Harry Potter Teile gelesen haben und nach jedem gelesenen Wort die Strichliste mit den einzelnen Wörtern einfach um einen Strich erweitert haben. Das Problem daran ist, dass wir diese Vorgehensweise nicht parallelisieren können. Angenommen eine zweite Person will uns unterstützen, dann kann sie das nicht tun, weil sie die Strichliste, mit der wir gerade arbeiten, benötigt, um weiterzumachen. Solange sie diese nicht hat, kann sie nicht unterstützen.

Sie kann uns aber unterstützen, indem sie bereits mit dem zweiten Teil der Harry Potter Reihe beginnt und eine eigene Strichliste nur für das zweite Buch erstellt. Zum Schluss können wir dann alle einzelnen Strichlisten zusammenführen und beispielsweise die Häufigkeit des Wortes “Harry” auf allen Strichlisten zusammenaddieren.

MapReduce am Beispiel von Wortzählungen in Harry Potter Büchern

MapReduce am Beispiel von Wortzählungen in Harry Potter Büchern | Source: Data Basecamp

Dadurch lässt sich die Aufgabe auch relativ einfach horizontal skalieren, indem jeweils eine Person pro Harry Potter Buch arbeitet. Wenn wir noch schneller arbeiten wollen, können wir auch mehrere Personen mit einbeziehen und jede Person ein einziges Kapitel bearbeiten lassen. Am Schluss müssen wir dann nur alle Ergebnisse der einzelnen Personen zusammennehmen, um so zu einem Gesamtergebnis zu gelangen.

Das ausführliche Beispiel und die Umsetzung in Python findest Du hier.

Aufbau eines Hadoop Distributed File Systems

Der Kern des Hadoop Distributed File Systems besteht darin die Daten auf verschiedene Dateien und Computer zu verteilen, sodass Abfragen schnell bearbeitet werden können und der Nutzer keine langen Wartezeiten hat. Damit der Ausfall einer einzelnen Maschine im Cluster nicht zum Verlust der Daten führt, gibt es gezielte Replikationen auf verschiedenen Computern, um eine Ausfallsicherheit zu gewährleisten.

Hadoop arbeitet im Allgemeinen nach dem sogenannten Master-Slave-Prinzip. Innerhalb des Computerclusters haben wir einen Knoten, der die Rolle des sogenannten Masters übernimmt. Dieser führt in unserem Beispiel keine direkte Berechnung durch, sondern verteilt lediglich die Aufgaben auf die sogenannten Slave Knoten und koordiniert den ganzen Prozess. Die Slave Knoten wiederum lesen die Bücher aus und speichern die Worthäufigkeit und die Wortverteilung.

Dieses Prinzip wird auch bei der Datenspeicherung genutzt. Der Master verteilt Informationen aus dem Datensatz auf verschiedenen Slave Nodes und merkt sich, auf welchen Computern er welche Partitionen abgespeichert hat. Dabei legt er die Daten auch redundant ab, um Ausfälle kompensieren zu können. Bei einer Abfrage der Daten durch den Nutzer entscheidet der Masterknoten dann, welche Slaveknoten er anfragen muss, um die gewünschten Informationen zu erhalten.

CAP Theorem

Understanding databases for storing, updating and analyzing data requires the understanding of the CAP Theorem. This is the second article of the article series Data Warehousing Basics.

Understanding NoSQL Databases by the CAP Theorem

CAP theorem – or Brewer’s theorem – was introduced by the computer scientist Eric Brewer at Symposium on Principles of Distributed computing in 2000. The CAP stands for Consistency, Availability and Partition tolerance.

  • Consistency: Every read receives the most recent writes or an error. Once a client writes a value to any server and gets a response, it is expected to get afresh and valid value back from any server or node of the database cluster it reads from.
    Be aware that the definition of consistency for CAP means something different than to ACID (relational consistency).
  • Availability: The database is not allowed to be unavailable because it is busy with requests. Every request received by a non-failing node in the system must result in a response. Whether you want to read or write you will get some response back. If the server has not crashed, it is not allowed to ignore the client’s requests.
  • Partition tolerance: Databases which store big data will use a cluster of nodes that distribute the connections evenly over the whole cluster. If this system has partition tolerance, it will continue to operate despite a number of messages being delayed or even lost by the network between the cluster nodes.

CAP theorem applies the logic that  for a distributed system it is only possible to simultaneously provide  two out of the above three guarantees. Eric Brewer, the father of the CAP theorem, proved that we are limited to two of three characteristics, “by explicitly handling partitions, designers can optimize consistency and availability, thereby achieving some trade-off of all three.” (Brewer, E., 2012).

CAP Theorem Triangle

To recap, with the CAP theorem in relation to Big Data distributed solutions (such as NoSQL databases), it is important to reiterate the fact, that in such distributed systems it is not possible to guarantee all three characteristics (Availability, Consistency, and Partition Tolerance) at the same time.

Database systems designed to fulfill traditional ACID guarantees like relational database (management) systems (RDBMS) choose consistency over availability, whereas NoSQL databases are mostly systems designed referring to the BASE philosophy which prefer availability over consistency.

The CAP Theorem in the real world

Lets look at some examples to understand the CAP Theorem further and provewe cannot create database systems which are being consistent, partition tolerant as well as always available simultaniously.

AP – Availability + Partition Tolerance

If we have achieved Availability (our databases will always respond to our requests) as well as Partition Tolerance (all nodes of the database will work even if they cannot communicate), it will immediately mean that we cannot provide Consistency as all nodes will go out of sync as soon as we write new information to one of the nodes. The nodes will continue to accept the database transactions each separately, but they cannot transfer the transaction between each other keeping them in synchronization. We therefore cannot fully guarantee the system consistency. When the partition is resolved, the AP databases typically resync the nodes to repair all inconsistencies in the system.

A well-known real world example of an AP system is the Domain Name System (DNS). This central network component is responsible for resolving domain names into IP addresses and focuses on the two properties of availability and failure tolerance. Thanks to the large number of servers, the system is available almost without exception. If a single DNS server fails,another one takes over. According to the CAP theorem, DNS is not consistent: If a DNS entry is changed, e.g. when a new domain has been registered or deleted, it can take a few days before this change is passed on to the entire system hierarchy and can be seen by all clients.

CA – Consistency + Availability

Guarantee of full Consistency and Availability is practically impossible to achieve in a system which distributes data over several nodes. We can have databases over more than one node online and available, and we keep the data consistent between these nodes, but the nature of computer networks (LAN, WAN) is that the connection can get interrupted, meaning we cannot guarantee the Partition Tolerance and therefor not the reliability of having the whole database service online at all times.

Database management systems based on the relational database models (RDBMS) are a good example of CA systems. These database systems are primarily characterized by a high level of consistency and strive for the highest possible availability. In case of doubt, however, availability can decrease in favor of consistency. Reliability by distributing data over partitions in order to make data reachable in any case – even if computer or network failure – meanwhile plays a subordinate role.

CP – Consistency + Partition Tolerance

If the Consistency of data is given – which means that the data between two or more nodes always contain the up-to-date information – and Partition Tolerance is given as well – which means that we are avoiding any desynchronization of our data between all nodes, then we will lose Availability as soon as only one a partition occurs between any two nodes In most distributed systems, high availability is one of the most important properties, which is why CP systems tend to be a rarity in practice. These systems prove their worth particularly in the financial sector: banking applications that must reliably debit and transfer amounts of money on the account side are dependent on consistency and reliability by consistent redundancies to always be able to rule out incorrect postings – even in the event of disruptions in the data traffic. If consistency and reliability is not guaranteed, the system might be unavailable for the users.

CAP Theorem Venn Diagram

Conclusion

The CAP Theorem is still an important topic to understand for data engineers and data scientists, but many modern databases enable us to switch between the possibilities within the CAP Theorem. For example, the Cosmos DB von Microsoft Azure offers many granular options to switch between the consistency, availability and partition tolerance . A common misunderstanding of the CAP theorem that it´s none-absoluteness: “All three properties are more continuous than binary. Availability is continuous from 0 to 100 percent, there are many levels of consistency, and even partitions have nuances. Exploring these nuances requires pushing the traditional way of dealing with partitions, which is the fundamental challenge. Because partitions are rare, CAP should allow perfect C and A most of the time, but when partitions are present or perceived, a strategy is in order.” (Brewer, E., 2012).

ACID vs BASE Concepts

Understanding databases for storing, updating and analyzing data requires the understanding of two concepts: ACID and BASE. This is the first article of the article series Data Warehousing Basics.

The properties of ACID are being applied for databases in order to fulfill enterprise requirements of reliability and consistency.

ACID is an acronym, and stands for:

  • Atomicity – Each transaction is either properly executed completely or does not happen at all. If the transaction was not finished the process reverts the database back to the state before the transaction started. This ensures that all data in the database is valid even if we execute big transactions which include multiple statements (e. g. SQL) composed into one transaction updating many data rows in the database. If one statement fails, the entire transaction will be aborted, and hence, no changes will be made.
  • Consistency – Databases are governed by specific rules defined by table formats (data types) and table relations as well as further functions like triggers. The consistency of data will stay reliable if transactions never endanger the structural integrity of the database. Therefor, it is not allowed to save data of different types into the same single column, to use written primary key values again or to delete data from a table which is strictly related to data in another table.
  • Isolation – Databases are multi-user systems where multiple transactions happen at the same time. With Isolation, transactions cannot compromise the integrity of other transactions by interacting with them while they are still in progress. It guarantees data tables will be in the same states with several transactions happening concurrently as they happen sequentially.
  • Durability – The data related to the completed transaction will persist even in cases of network or power outages. Databases that guarante Durability save data inserted or updated permanently, save all executed and planed transactions in a recording and ensure availability of the data committed via transaction even after a power failure or other system failures If a transaction fails to complete successfully because of a technical failure, it will not transform the targeted data.

ACID Databases

The ACID transaction model ensures that all performed transactions will result in reliable and consistent databases. This suits best for businesses which use OLTP (Online Transaction Processing) for IT-Systems such like ERP- or CRM-Systems. Furthermore, it can also be a good choice for OLAP (Online Analytical Processing) which is used in Data Warehouses. These applications need backend database systems which can handle many small- or medium-sized transactions occurring simultaneous by many users. An interrupted transaction with write-access must be removed from the database immediately as it could cause negative side effects impacting the consistency(e.g., vendors could be deleted although they still have open purchase orders or financial payments could be debited from one account and due to technical failure, never credited to another).

The speed of the querying should be as fast as possible, but even more important for those applications is zero tolerance for invalid states which is prevented by using ACID-conform databases.

BASE Concept

ACID databases have their advantages but also one big tradeoff: If all transactions need to be committed and checked for consistency correctly, the databases are slow in reading and writing data. Furthermore, they demand more effort if it comes to storing new data in new formats.

In chemistry, a base is the opposite to acid. The database concepts of BASE and ACID have a similar relationship. The BASE concept provides several benefits over ACID compliant databases asthey focus more intensely on data availability of database systems without guarantee of safety from network failures or inconsistency.

The acronym BASE is even more confusing than ACID as BASE relates to ACID indirectly. The words behind BASE suggest alternatives to ACID.

BASE stands for:

  • Basically Available – Rather than enforcing consistency in any case, BASE databases will guarantee availability of data by spreading and replicating it across the nodes of the database cluster. Basic read and write functionality is provided without liabilityfor consistency. In rare cases it could happen that an insert- or update-statement does not result in persistently stored data. Read queries might not provide the latest data.
  • Soft State – Databases following this concept do not check rules to stay write-consistent or mutually consistent. The user can toss all data into the database, delegating the responsibility of avoiding inconsistency or redundancy to developers or users.
  • Eventually Consistent –No guarantee of enforced immediate consistency does not mean that the database never achieves it. The database can become consistent over time. After a waiting period, updates will ripple through all cluster nodes of the database. However, reading data out of it will stay always be possible, it is just not certain if we always get the last refreshed data.

All the three above mentioned properties of BASE-conforming databases sound like disadvantages. So why would you choose BASE? There is a tradeoff compared to ACID. If databases do not have to follow ACID properties then the database can work much faster in terms of writing and reading from the database. Further, the developers have more freedom to implement data storage solutions or simplify data entry into the database without thinking about formats and structure beforehand.

BASE Databases

While ACID databases are mostly RDBMS, most other database types, known as NoSQL databases, tend more to conform to BASE principles. Redis, CouchDB, MongoDB, Cosmos DB, Cassandra, ElasticSearch, Neo4J, OrientDB or ArangoDB are just some popular examples. But other than ACID, BASE is not a strict approach. Some NoSQL databases apply at least partly to ACID rules or provide optional functions to get almost or even full ACID compatibility. These databases provide different level of freedom which can be useful for the Staging Layer in Data Warehouses or as a Data Lake, but they are not the recommended choice for applications which need data environments guaranteeing strict consistency.

Data Warehousing Basiscs

Data Warehousing is applied Big Data Management and a key success factor in almost every company. Without a data warehouse, no company today can control its processes and make the right decisions on a strategic level as there would be a lack of data transparency for all decision makers. Bigger comanies even have multiple data warehouses for different purposes.

In this series of articles I would like to explain what a data warehouse actually is and how it is set up. However, I would also like to explain basic topics regarding Data Engineering and concepts about databases and data flows.

To do this, we tick off the following points step by step:

 

How to Efficiently Manage Big Data

The benefits of big data today can’t be ignored, especially since these benefits encompass industries. Despite the misconceptions around big data, it has shown potential in helping organizations move forward and adapt to an ever-changing market, where those that can’t respond appropriately or quickly enough are left behind. Data analytics is the name of the game, and efficient data management is the main differentiator.

A digital integration hub (DIH) will provide the competitive edge an organization needs in the efficient handling of data. It’s an application architecture that aggregates operational data into a low-latency data fabric and helps in digital transformation by offloading from legacy architecture and providing a decoupled API layer that effectively supports modern online applications. Data management entails the governance, organization, and administration of large, and possibly complex, datasets. The rapid growth of data pooIs has left unprepared companies scrambling to find solutions that will help keep them above water. Data in these pools originate from a myriad of sources, including websites, social media sites, and system logs. The variety in data types and their sheer size make data management a fairly complex undertaking.

Big Data Management for Big Wins

In today’s data-driven world, the capability to efficiently analyze and store data are vital factors in enhancing current business processes and setting up new ones. Data has gone beyond the realm of data analysts and into the business mainstream. As such, businesses should add data analysis as a core competency to ensure that the entire organization is on the same page when it comes to data strategy. Below are a few ways you can make big data work for you and your business.

Define Specific Goals

The data you need to capture will depend on your business goals so it’s imperative that you know what these are and ensure that these are shared across the organization. Without definite and specific goals, you’ll end up with large pools of data and nowhere to use them in. As such, it’s advisable to involve the entire team in mapping out a data strategy based on the company’s objectives. This strategy should be part of the organization’s overall business strategy to avoid the collection of irrelevant data that has no impact on business performance. Setting the direction early on will help set you up for long-term success.

Secure Your Data

Because companies have to contend with large amounts of data each day, storage and management could become very challenging. Security is also a main concern; no organization wants to lose it’s precious data after spending time and money processing and storing them. While keeping data accessible for analysis, you should also ensure that it’s kept secure at all times. When handling data, you should have security measures in place, such as firewall security, malware scanning, and spam filtering. Data security is especially important when collecting customer data to avoid violating data privacy regulations. Ideally, it should be one of the main considerations in data management because it’s a critical factor that could mean the difference between a successful venture and a problematic one.

Interlink Your Data

Different channels can be used to access a database, but this doesn’t necessarily mean that you should use several or all of them. There’s no need to deploy different tools for each application your organization uses. One of the ways to prevent miscommunication between applications and ensure that data is synchronized at all times is to keep data interlinked. Synchronicity of data is vital if your organization or team plans to use a single database. An in-memory data grid, cloud storage system, and remote database administrator are just some of the tools a company can use to interlink data.

Ensure Compliance With Audit Regulations

One thing that could be easy to overlook is how compliant systems are to audit regulations. A database and it is conducted to check on the actions of database users and managers. It is typically done for security purposes—to ensure that data or information can be accessed only by those authorized to do so. Adhering to audit regulations is a must, even for offsite database administrators, so it’s critical that they maintain compliant database components.

Be Prepared for Change

There have been significant changes in the field of data processing and management in recent years, which indicates a promising yet constantly changing landscape. To get ahead of the data analytics game, it’s vital that you keep up with current data trends. New tools and technology are made available at an almost regular pace, and keeping abreast of them will ensure that a business keeps its database up to date. It’s also important to be flexible and be able to pivot or restrategize at a moment’s notice so the business can adapt to change accordingly.

Big Data for the Long Haul

Traditional data warehouses and relational database platforms are slowly becoming things of the past. Big data analytics has changed the game, with data management moving away from being a complex, IT-team focused function and becoming a core competency of every business. Ensuring efficient data management means giving your business a competitive edge, and implementing the tips above ensures that a business manages its data effectively. Changes in data strategies are certain, and they may come sooner than later. Equipping your people with the appropriate skills and knowledge will ensure that your business can embrace change with ease.

Zertifikatsstudium „Data Science and Big Data“ 2021 an der TU Dortmund

Anzeige

Komplexe Daten aufbereiten und analysieren, um daraus zukünftige Entwicklungen abzulesen: das lernen Sie im berufsbegleitenden Zertifikatsstudium „Data Science and Big Data“ an der TU Dortmund.

Jetzt bewerben!

Data Science & Big Data 2021

Die Zielgruppe sind Fachkräfte, die sich in ihrer Berufspraxis mit Fragestellungen zum Thema Datenanalyse und Big Data befassen, jedoch nun tiefergehende Kenntnisse in dem Themenfeld erhalten möchten. Von der Analyse über das Management bis zur zielgerichteten Darstellung der Ergebnisse lernen die Teilnehmenden dabei Methoden der Disziplinen Statistik, Informatik und Journalistik kennen.

Renommierte Wissenschaftlerinnen und Wissenschaftler vermitteln den Teilnehmerinnen und Teilnehmern die neuesten datenwissenschaftlichen Erkenntnisse und zeigen, wie dieses Wissen praxisnah im eigenen Big-Data Projekt umgesetzt werden kann.

Die nächste Studiengruppe startet im Februar 2021, der Bewerbungsschluss ist am 2. November 2020. Die Anzahl der verfügbaren Plätze ist begrenzt, eine rechtzeitige Bewerbung lohnt sich daher.

Nähere Informationen finden Sie unter: http://www.zhb.tu-dortmund.de/datascience

Simple RNN

Simple RNN: the first foothold for understanding LSTM

*In this article “Densely Connected Layers” is written as “DCL,” and “Convolutional Neural Network” as “CNN.”

In the last article, I mentioned “When it comes to the structure of RNN, many study materials try to avoid showing that RNNs are also connections of neurons, as well as DCL or CNN.” Even if you manage to understand DCL and CNN, you can be suddenly left behind once you try to understand RNN because it looks like a different field. In the second section of this article, I am going to provide a some helps for more abstract understandings of DCL/CNN , which you need when you read most other study materials.

My explanation on this simple RNN is based on a chapter in a textbook published by Massachusetts Institute of Technology, which is also recommended in some deep learning courses of Stanford University.

First of all, you should keep it in mind that simple RNN are not useful in many cases, mainly because of vanishing/exploding gradient problem, which I am going to explain in the next article. LSTM is one major type of RNN used for tackling those problems. But without clear understanding forward/back propagation of RNN, I think many people would get stuck when they try to understand how LSTM works, especially during its back propagation stage. If you have tried climbing the mountain of understanding LSTM, but found yourself having to retreat back to the foot, I suggest that you read through this article on simple RNNs. It should help you to gain a solid foothold, and you would be ready for trying to climb the mountain again.

*This article is the second article of “A gentle introduction to the tiresome part of understanding RNN.”

1, A brief review on back propagation of DCL.

Simple RNNs are straightforward applications of DCL, but if you do not even have any ideas on DCL forward/back propagation, you will not be able to understand this article. If you more or less understand how back propagation of DCL works, you can skip this first section.

Deep learning is a part of machine learning. And most importantly, whether it is classical machine learning or deep learning, adjusting parameters is what machine learning is all about. Parameters mean elements of functions except for variants. For example when you get a very simple function f(x)=a + bx + cx^2 + dx^3, then x is a variant, and a, b, c, d are parameters. In case of classical machine learning algorithms, the number of those parameters are very limited because they were originally designed manually. Such functions for classical machine learning is useful for features found by humans, after trial and errors(feature engineering is a field of finding such effective features, manually). You adjust those parameters based on how different the outputs(estimated outcome of classification/regression) are from supervising vectors(the data prepared to show ideal answers).

In the last article I said neural networks are just mappings, whose inputs are vectors, matrices, or sequence data. In case of DCLs, inputs are vectors. Then what’s the number of parameters ? The answer depends on the the number of neurons and layers. In the example of DCL at the right side, the number of the connections of the neurons is the number of parameters(Would you like to try to count them? At least I would say “No.”). Unlike classical machine learning you no longer need to do feature engineering, but instead you need to design networks effective for each task and adjust a lot of parameters.

*I think the hype of AI comes from the fact that neural networks find features automatically. But the reality is difficulty of feature engineering was just replaced by difficulty of designing proper neural networks.

It is easy to imagine that you need an efficient way to adjust those parameters, and the method is called back propagation (or just backprop). As long as it is about DCL backprop, you can find a lot of well-made study materials on that, so I am not going to cover that topic precisely in this article series. Simply putting, during back propagation, in order to adjust parameters of a layer you need errors in the next layer. And in order calculate the errors of the next layer, you need errors in the next next layer.

*You should not think too much about what the “errors” exactly mean. Such “errors” are defined in this context, and you will see why you need them if you actually write down all the mathematical equations behind backprops of DCL.

The red arrows in the figure shows how errors of all the neurons in a layer propagate backward to a neuron in last layer. The figure shows only some sets of such errors propagating backward, but in practice you have to think about all the combinations of such red arrows in the whole back propagation(this link would give you some ideas on how DCLs work).

These points are minimum prerequisites for continuing reading this  RNN this article. But if you are planning to understand RNN forward/back propagation at  an abstract/mathematical level that you can read academic papers,  I highly recommend you to actually write down all the equations of DCL backprop. And if possible you should try to implement backprop of three-layer DCL.

2, Forward propagation of simple RNN

*For better understandings of the second and third section, I recommend you to download an animated PowerPoint slide which I prepared. It should help you understand simple RNNs.

In fact the simple RNN which we are going to look at in this article has only three layers. From now on imagine that inputs of RNN come from the bottom and outputs go up. But RNNs have to keep information of earlier times steps during upcoming several time steps because as I mentioned in the last article RNNs are used for sequence data, the order of whose elements is important. In order to do that, information of the neurons in the middle layer of RNN propagate forward to the middle layer itself. Therefore in one time step of forward propagation of RNN, the input at the time step propagates forward as normal DCL, and the RNN gives out an output at the time step. And information of one neuron in the middle layer propagate forward to the other neurons like yellow arrows in the figure. And the information in the next neuron propagate forward to the other neurons, and this process is repeated. This is called recurrent connections of RNN.

*To be exact we are just looking at a type of recurrent connections. For example Elman RNNs have simpler recurrent connections. And recurrent connections of LSTM are more complicated.

Whether it is a simple one or not, basically RNN repeats this process of getting an input at every time step, giving out an output, and making recurrent connections to the RNN itself. But you need to keep the values of activated neurons at every time step, so virtually you need to consider the same RNNs duplicated for several time steps like the figure below. This is the idea of unfolding RNN. Depending on contexts, the whole unfolded DCLs with recurrent connections is also called an RNN.

In many situations, RNNs are simplified as below. If you have read through this article until this point, I bet you gained some better understanding of RNNs, so you should little by little get used to this more abstract, blackboxed  way of showing RNN.

You have seen that you can unfold an RNN, per time step. From now on I am going to show the simple RNN in a simpler way,  based on the MIT textbook which I recomment. The figure below shows how RNN propagate forward during two time steps (t-1), (t).

The input \boldsymbol{x}^{(t-1)}at time step(t-1) propagate forward as a normal DCL, and gives out the output \hat{\boldsymbol{y}} ^{(t)} (The notation on the \boldsymbol{y} ^{(t)} is called “hat,” and it means that the value is an estimated value. Whatever machine learning tasks you work on, the outputs of the functions are just estimations of ideal outcomes. You need to adjust parameters for better estimations. You should always be careful whether it is an actual value or an estimated value in the context of machine learning or statistics). But the most important parts are the middle layers.

*To be exact I should have drawn the middle layers as connections of two layers of neurons like the figure at the right side. But I made my figure closer to the chart in the MIT textbook, and also most other study materials show the combinations of the two neurons before/after activation as one neuron.

\boldsymbol{a}^{(t)} is just linear summations of \boldsymbol{x}^{(t)} (If you do not know what “linear summations” mean, please scroll this page a bit), and \boldsymbol{h}^{(t)} is a combination of activated values of \boldsymbol{a}^{(t)} and linear summations of \boldsymbol{h}^{(t-1)} from the last time step, with recurrent connections. The values of \boldsymbol{h}^{(t)} propagate forward in two ways. One is normal DCL forward propagation to \hat{\boldsymbol{y}} ^{(t)} and \boldsymbol{o}^{(t)}, and the other is recurrent connections to \boldsymbol{h}^{(t+1)} .

These are equations for each step of forward propagation.

  • \boldsymbol{a}^{(t)} = \boldsymbol{b} + \boldsymbol{W} \cdot \boldsymbol{h}^{(t-1)} + \boldsymbol{U} \cdot \boldsymbol{x}^{(t)}
  • \boldsymbol{h}^{(t)}= g(\boldsymbol{a}^{(t)})
  • \boldsymbol{o}^{(t)} = \boldsymbol{c} + \boldsymbol{V} \cdot \boldsymbol{h}^{(t)}
  • \hat{\boldsymbol{y}} ^{(t)} = f(\boldsymbol{o}^{(t)})

*Please forgive me for adding some mathematical equations on this article even though I pledged not to in the first article. You can skip the them, but for some people it is on the contrary more confusing if there are no equations. In case you are allergic to mathematics, I prescribed some treatments below.

*Linear summation is a type of weighted summation of some elements. Concretely, when you have a vector \boldsymbol{x}=(x_0, x_1, x_2), and weights \boldsymbol{w}=(w_0,w_1, w_2), then \boldsymbol{w}^T \cdot \boldsymbol{x} = w_0 \cdot x_0 + w_1 \cdot x_1 +w_2 \cdot x_2 is a linear summation of \boldsymbol{x}, and its weights are \boldsymbol{w}.

*When you see a product of a matrix and a vector, for example a product of \boldsymbol{W} and \boldsymbol{v}, you should clearly make an image of connections between two layers of a neural network. You can also say each element of \boldsymbol{u}} is a linear summations all the elements of \boldsymbol{v}} , and \boldsymbol{W} gives the weights for the summations.

A very important point is that you share the same parameters, in this case \boldsymbol{\theta \in \{\boldsymbol{U}, \boldsymbol{W}, \boldsymbol{b}, \boldsymbol{V}, \boldsymbol{c} \}}, at every time step. 

And you are likely to see this RNN in this blackboxed form.

3, The steps of back propagation of simple RNN

In the last article, I said “I have to say backprop of RNN, especially LSTM (a useful and mainstream type or RNN), is a monster of chain rules.” I did my best to make my PowerPoint on LSTM backprop straightforward. But looking at it again, the LSTM backprop part still looks like an electronic circuit, and it requires some patience from you to understand it. If you want to understand LSTM at a more mathematical level, understanding the flow of simple RNN backprop is indispensable, so I would like you to be patient while understanding this step (and you have to be even more patient while understanding LSTM backprop).

This might be a matter of my literacy, but explanations on RNN backprop are very frustrating for me in the points below.

  • Most explanations just show how to calculate gradients at each time step.
  • Most study materials are visually very poor.
  • Most explanations just emphasize that “errors are back propagating through time,” using tons of arrows, but they lack concrete instructions on how actually you renew parameters with those errors.

If you can relate to the feelings I mentioned above, the instructions from now on could somewhat help you. And with the animated PowerPoint slide I prepared, you would have clear understandings on this topic at a more mathematical level.

Backprop of RNN , as long as you are thinking about simple RNNs, is not so different from that of DCLs. But you have to be careful about the meaning of errors in the context of RNN backprop. Back propagation through time (BPTT) is one of the major methods for RNN backprop, and I am sure most textbooks explain BPTT. But most study materials just emphasize that you need errors from all the time steps, and I think that is very misleading and confusing.

You need all the gradients to adjust parameters, but you do not necessarily need all the errors to calculate those gradients. Gradients in the context of machine learning mean partial derivatives of error functions (in this case J) with respect to certain parameters, and mathematically a gradient of J with respect to \boldsymbol{\theta \in \{\boldsymbol{U}, \boldsymbol{W}, \boldsymbol{b}^{(t)}, \boldsymbol{V}, \boldsymbol{c} \}}is denoted as ( \frac{\partial J}{\partial \boldsymbol{\theta}}  ). And another confusing point in many textbooks, including the MIT one, is that they give an impression that parameters depend on time steps. For example some study materials use notations like \frac{\partial J}{\partial \boldsymbol{\theta}^{(t)}}, and I think this gives an impression that this is a gradient with respect to the parameters at time step (t). In my opinion this gradient rather should be written as ( \frac{\partial J}{\partial \boldsymbol{\theta}} )^{(t)} . But many study materials denote gradients of those errors in the former way, so from now on let me use the notations which you can see in the figures in this article.

In order to calculate the gradient \frac{\partial J}{\partial \boldsymbol{x}^{(t)}} you need errors from time steps s (s \geq t) \quad (as you can see in the figure, in order to calculate a gradient in a colored frame, you need all the errors in the same color).

*To be exact, in the figure above I am supposed prepare much more arrows in \tau + 1 different colors  to show the whole process of RNN backprop, but that is not realistic. In the figure I displayed only the flows of errors necessary for calculating each gradient at time step 0, t, \tau.

*Another confusing point is that the \frac{\partial J}{\partial \boldsymbol{\ast ^{(t)}}}, \boldsymbol{\ast} \in \{\boldsymbol{a}^{(t)}, \boldsymbol{h}^{(t)}, \boldsymbol{o}^{(t)}, \dots \} are correct notations, because \boldsymbol{\ast} are values of neurons after forward propagation. They depend on time steps, and these are very values which I have been calling “errors.” That is why parameters do not depend on time steps, whereas errors depend on time steps.

As I mentioned before, you share the same parameters at every time step. Again, please do not assume that parameters are different from time step to time step. It is gradients/errors (you need errors to calculate gradients) which depend on time step. And after calculating errors at every time step, you can finally adjust parameters one time, and that’s why this is called “back propagation through time.” (It is easy to imagine that this method can be very inefficient. If the input is the whole text on a Wikipedia link, you need to input all the sentences in the Wikipedia text to renew parameters one time. To solve this problem there is a backprop method named “truncated BPTT,” with which you renew parameters based on a part of a text. )

And after calculating those gradients \frac{\partial J}{\partial \boldsymbol{\theta}^{(t)}} you can take a summation of them: \frac{\partial J}{\partial \boldsymbol{\theta}}=\sum_{t=0}^{t=\tau}{\frac{\partial J}{\partial \boldsymbol{\theta}^{(t)}}}. With this gradient \frac{\partial J}{\partial \boldsymbol{\theta}} , you can finally renew the value of \boldsymbol{\theta} one time.

At the beginning of this article I mentioned that simple RNNs are no longer for practical uses, and that comes from exploding/vanishing problem of RNN. This problem was one of the reasons for the AI winter which lasted for some 20 years. In the next article I am going to write about LSTM, a fancier type of RNN, in the context of a history of neural network history.

* I make study materials on machine learning, sponsored by DATANOMIQ. I do my best to make my content as straightforward but as precise as possible. I include all of my reference sources. If you notice any mistakes in my materials, including grammatical errors, please let me know (email: yasuto.tamura@datanomiq.de). And if you have any advice for making my materials more understandable to learners, I would appreciate hearing it.

Data Analytics & Artificial Intelligence Trends in 2020

Artificial intelligence has infiltrated all aspects of our lives and brought significant improvements.

Although the first thing that comes to most people’s minds when they think about AI are humanoid robots or intelligent machines from sci-fi flicks, this technology has had the most impressive advancements in the field of data science.

Big data analytics is what has already transformed the way we do business as it provides an unprecedented insight into a vast amount of unstructured, semi-structured, and structured data by analyzing, processing, and interpreting it.

Data and AI specialists and researchers are likely to have a field day in 2020, so here are some of the most important trends in this industry.

1. Predictive Analytics

As its name suggests, this trend will be all about using gargantuan data sets in order to predict outcomes and results.

This practice is slated to become one of the biggest trends in 2020 because it will help businesses improve their processes tremendously. It will find its place in optimizing customer support, pricing, supply chain, recruitment, and retail sales, to name just a few.

For example, Amazon has already been leveraging predictive analytics for its dynamic pricing model. Namely, the online retail giant uses this technology to analyze the demand for a particular product, competitors’ prices, and a number of other parameters in order to adjust its price.

According to stats, Amazon changes prices 2.5 million times a day so that a particular product’s cost fluctuates and changes every 10 minutes, which requires an extremely predictive analytics algorithm.

2. Improved Cybersecurity

In a world of advanced technologies where IoT and remotely controlled devices having top-notch protection is of critical importance.

Numerous businesses and individuals have fallen victim to ruthless criminals who can steal sensitive data or wipe out entire bank accounts. Even some big and powerful companies suffered huge financial and reputation blows due to cyber attacks they were subjected to.

This kind of crime is particularly harsh for small and medium businesses. Stats say that 60% of SMBs are forced to close down after being hit by such an attack.

AI again takes advantage of its immense potential for analyzing and processing data from different sources quickly and accurately. That’s why it’s capable of assisting cybersecurity specialists in predicting and preventing attacks.

In case that an attack emerges, the response time is significantly shorter, so that the worst-case scenario can be avoided.

When we’re talking about avoiding security risks, AI can improve enterprise risk management, too, by providing guidance and assisting risk management professionals.

3. Digital Workers

In 2020, an army of digital workers will transform the traditional workspace and take productivity to a whole new level.

Virtual assistants and chatbots are some examples of already existing digital workers, but it will be even more of them. According to research, this trend is one the rise, as it’s expected that AI software and robots will increase by 50% by 2022.

Robots will take over even some small tasks in the office. The point is to streamline the entire business process, and that can be achieved by training robots to perform small and simple tasks like human employees. The only difference will be that digital workers will do that faster and without any mistakes.

4. Hybrid Workforce

Many people worry that AI and automation will steal their jobs and render them unemployed.

Even the stats are bleak – AI will eliminate 1.8 million jobs. But, on the other hand, it will create 2.3 million new jobs.

So, our future is actually AI and humans working together, and that’s what will become the business normalcy in 2020.

Robotic process automation and different office digital workers will be in charge of tedious and repetitive tasks, while more sophisticated issues that require critical thinking and creativity will be human workers’ responsibility.

One of the most important things about creating this hybrid workforce is for businesses to openly discuss it with their employees and explain how these new technologies will be used. A regular workforce has to know that they will be working alongside machines whose job will be to speed up the processes and cut costs.

5. Process Intelligence

This AI trend will allow businesses to gain insight into their processes by using all the information contained in their system and creating an overall, real-time, and accurate visual model of all the processes.

What’s great about it is that it’s possible to see these processes from different perspectives – across departments, functions, staff, and locations.

With such a visual model, it’s possible to properly analyze these processes, identify potential bottlenecks, and eliminate them before they even begin to emerge.

Besides, as this is AI and data analytics at their best, this technology will also facilitate decision-making by predicting the future results of tech investments.

Needless to say, Process Intelligence will become an enterprise standard very soon, thanks to its ability to provide a better understanding and effective management of end-to-end processes.

As you can see, in 2020, these two advanced technologies will continue to evolve and transform the business landscape and change it for the better.