Join us for the global launch of Tableau’s super fast data engine, Hyper! Hyper brings faster data refreshes and query performance to Tableau extracts, plus increased scalability in a platform-wide update.
This is your opportunity to get to know the Hyper dev team, hear from Tableau beta customers about their hands-on Hyper experience, and participate in live Q&A. Best of all, learn more about Hyper’s patent-pending technology as well as some of the other features headed your way in 10.5. (Viz in Tooltip, anyone?)
Tableau is hosting the Hyperfest meetup – come and celebrate with the community and the world on the upcoming release of Hyper. In addition to the Hyper presentation, we will also have food, drinks and Tableau swag, so don’t miss it!
The Future of Enterprise Analytics: Hyper can handle both OLTP and OLAP simultaneously. In the future it will address NoSQL and graph workloads.
1. What is Hyper’s key benefit?
Hyper is a Hybrid transactional/analytical processing (HTAP) database system and replaces Tableau Data Extracts (TDE). The change will be mostly transparent for end users, other than everything being faster. Hyper significantly improves extract refresh times, query times and overall performance.
2. What is Hybrid transactional/analytical processing?
Hybrid transaction/analytical processing (HTAP) is an emerging application architecture that „breaks the wall“ between transaction processing and analytics. It enables more informed and „in business real time“ decision making.
The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load (ETL) data staging and excessive resource consumption due to maintaining two separate information systems.
3. Does Hyper satisfy the ACID properties?
Hyper, initially developed at the Technical University of Munich and acquired by Tableau in 2016, can handle both OLTP and OLAP simultaneously. Hyper possesses the rare quality of being able to handle data updates and insertions at the same time as queries by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. Hyper is an in-memory database that guarantees the ACID properties (Atomicity, Consistency, Isolation, Durability) of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot.
4. What makes Hyper so fast?
The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100,000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. This would support real-time streaming of data in future releases of Tableau. These performance increases come from the nature of the Hyper data structures, but also from smart use of contemporary hardware technology, and particularly nvRam memory. Additional cores provide a linear increment in performance.
5. What does this mean for Tableau?
With Hyper now powering the Tableau platform, your organization will see faster extract creation and better query performance for large data sets. Since Hyper is designed to handle exceptionally large data sets, you can choose to extract your data based on what you need, not data volume limitations. Hyper improves performance for common computationally-intensive queries, like count distinct, calculated fields, and text field manipulations. This performance boost will improve your entire Enterprise Analytics workflow.
Julia in Tableau: R allows Tableau to execute Julia code on the fly, enhancing your data analytics experience.
Michael, a data scientist, who is working for a German railway and logistics company, recently told me during a FATUG Meetup that he loves Tableau’s R integration and Tableau’s Python integration. As he continued, he raised the question of using functions they have written in Julia. Julia, a high-level dynamic programming language for high-performance numerical analysis, is an integral part of the newly developed data strategy in Michael’s organization.
Tableau, however, does not come with native support for Julia. I didn’t want to keep Michael’s team down and was looking for an alternative way to integrate Julia with Tableau.
This solution is working flawlessly in a production environment for several months. In this tutorial, I’m going to walk you through the installation and connecting Tableau with R and Julia. I will also give you an example of calling a Julia statement from Tableau to calculate the sphere volume.
XRJulia provides an interface from R to Julia. RServe is a TCP/IP server that allows Tableau to use facilities of R.
3. Load libraries and start RServe
After packages are successfully installed, we load them and run RServe:
> library(XRJulia) > library(Rserve) > Rserve()
Make sure to repeat this step every time you restart your R session.
4. Connecting Tableau to RServe
Now let’s open the Help menu in Tableau Desktop and choose Settings and Performance >Manage External Service connection to open the External Service Connection dialog box:
Enter a server name using a domain or an IP address and specify a port. Port 6311 is the default port used by Rserve. Take a look at my R tutorial to learn more about Tableau’s R integration.
5. Adding Julia code to a Calculated Field
You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Julia code in Tableau, such as this simple snippet that calculates sphere volume:
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You can now use your Julia calculation as an alternate Calculated Field in your Tableau worksheet:
Using Julia calculations within Tableau (click to enlarge)
Feel free to download the Tableau Packaged Workbook (twbx) here.
Further Reading: Mastering Julia
If you want to go beyond this tutorial and explore more about Julia in the context of data science, I recommend the book Mastering Julia. You can find it here.
Further Reading: Visual Analytics with Tableau
Join the data science conversation and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Julia in Tableau and other data analytics topics. If you’re looking to master Tableau, don’t forget to preorder your copy of my upcoming book, Visual Analytics with Tableau. (Amazon). It offers an in-depth exploration of data visualization techniques and best practices.
Abbildung 4: Interaktives Dashboard zur Darstellung von variablen Abhängigkeiten mit TensorFlow in Tableau
Der erste Schritt auf dem Weg zu besserer Entscheidungsfindung im Unternehmen, ist zu verstehen, wie gute (oder schlechte) Entscheidungen zustande kamen. Genau wie manche Unternehmen formale Prozesse für Aktivitäten haben, wie z. B. What-if-Analysen, prädiktive Wartung und Bestimmung von Abhängigkeiten in Korrelationen (siehe Abbildung 4), so müssen sie formale Prüfprozesse für Entscheidungen im gesamten Unternehmen einführen. Dies soll jedoch keinesfalls dazu dienen, die an schlechten Entscheidungen Beteiligten zu bestrafen, sondern den Entscheidungsfindungsprozess und -stil des Unternehmens im Allgemeinen verbessern.
Die Rolle der IT nähert sich hierbei wieder ihren Wurzeln an und statt eine Berichtefabrik für den Rest des Unternehmens zu unterhalten, wird die IT wieder zum Dienstleister und Partner, der die Infrastruktur für eine Data Discovery bereitstellt. IT-Mitarbeiter werden entlastet und erhalten den Freiraum, ihre professionelle Energie und Kreativität in den Dienst der Innovation zu stellen, und die Mitarbeiter in den Abteilungen sehen ihre Datenfragen nicht am Flaschenhals Berichtswesen verhungern. Nur so lassen sich die Investitionen in Business Intelligence und Analytics optimal in den Dienst der strategischen Ziele des Unternehmens stellen.
Abbildung 5: Anforderungen, Fähigkeiten und Ziele einer Datenstrategie (TC17-Präsentation)
Moderne Unternehmen sehen sich vielen analytischen Anforderungen (siehe Abbildung 5) gegenüber, und diese Anforderungen werden unweigerlich schneller wachsen, als Unternehmen sie bedienen können. Es ist daher unerlässlich, Analytics als lebenswichtigen Teil der eigenen Datenstrategie zu verstehen und entsprechend zu planen.
Dabei ist ein umfassender Betrachtungswinkel sinnvoll, denn die wachsende Nachfrage nach Analysen und Erkenntnissen wird mehr und mehr von den kundenbezogenen Abteilungen wie Marketing oder Support ausgehen. Dementsprechend wird auch das Budget für Analytics verstärkt aus diesen Abteilungen kommen, statt aus einem zentralisierten IT- oder BI-Budget. Dort, wo viele Kundendaten vorhanden sind, wird der CMO bald mehr für Analytics ausgeben als der CIO. Und dort, wo Mitarbeiter über gut integrierte, intuitive Werkzeuge für komplexe Analysen verfügen, können gute Instinkte und datenbasierte Entscheidungen Hand in Hand für den Erfolg sorgen.
Möchten Sie mehr zu den neuesten Trends im Bereich Datenstrategie erfahren? Dann freue ich mich, wenn Sie an meinem Vortrag „Building an Enterprise Big Data & Advanced Analytics Strategy“ auf unserer Tableau Conference TC17 (9.-12. Okt., Las Vegas) teilnehmen.
Demo: using Julia calculations within Tableau (click to enlarge)
We have already seen some love from Tableau for R and Python, boosting Tableau’s Advanced Analytics capabilities.
So what is the next big thing for our Data Science Rockstars? Julia!
Who is Julia?
Julia is a high-level dynamic programming language introduced in 2012. Designed to address the needs of high-performance numerical analysis its syntax is very similar to MATLAB. If you are used to MATLAB, you will be very quick to get on track with Julia.
Compared to R and Python, Julia is significantly faster (close to C and FORTRAN, see benchmark). Based on Tableau’s R integration, Julia is a fantastic addition to Tableau’s Advanced Analytics stack and to your data science toolbox.
Where can I learn more?
Do you want to learn more about Advanced Analytics and how to leverage Tableau with R, Python, and Julia? Meet me at the 2017 Tableau Conferences in London, Berlin, or Las Vegas and join my Advanced Analytics sessions:
Yes, of course! I published tutorials for R and Python on this blog. And I will also publish a Julia tutorial soon. Feel free to follow me on Twitter @xlth, and leave me your feedback/suggestions in the comment section below.
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is a high-level dynamic programming language introduced in 2012. Designed to address the needs of high-performance numerical analysis its syntax is very similar to 