Data Analytics for Online Shops

Making shopping a smooth and easy experience is the goal of many online shops. But how can easy website navigation be optimized? Unlike the physical shops, you cannot talk to the sales people in the online shops and a customer should find his or her way through the online shop. And unlike pysical shops, online shops can have hundreds of thousands of visitors every day.

To optimize the shopping experience, some important questions should be answered :

• Why do customers put products into their shopping cart and do not buy them at the end?
• Was it because of the sales process or he/she liked another product simply better?
• What impact has a customer's drop from a detail page?
• Where are the differences in customer behavior depending on the different product groups,
• Customer types or assortments?
• What are the figures compared to the previous year?
• Were there any technical errors? Was the accessibility of all pages always guaranteed?

Traditionally, answering these questions require a lot of work and usually takes a few days a month to prepare answers and presentations for the management. Since the process is slow and requires many man-days, most companies cannot perform it anytime they want or in the frequency they need.

German business consultancy company Mayato automatize this process using data preparation, analytics and visualization tools.

The company extracts data from the operational database to in-memory, ultra-fast analytics database named EXASOL. Traditionally, large volumes of data need to be extracted from the database, loaded into R statistical language based scripts and run; and then loaded back to database for analysis and visualization.

Markus Dill, Managing Director at Mayato, explains the importance of good analytics for companies in the manufacturing sector and why having an analytic database that is low-maintenance such as EXASOL is critical for business success.

But EXASOL has built-in R capabilities so R scripts can be run on EXASOLs ultra-fast and high performance analytics environment. The company uses Talend ETL tool for automated data extraction, transformation and load. The data extraction, crunching (using R) and data visualization (in Tableau software) are all automated without human intervention.

The combination of Talend, EXASOL and Tableau fully automated the process and reduced its report generation cycle from days to hours.

Big Data Analytics in real-time with EXASOL and Tableau webinar

If you are using Tableau’s great BI, reporting and visualization software to see and understand your data, but are struggling with performance or just want to overcome the limitations of Tableau Data Extracts (TDE), EXASOL is the solution you need. Using EXASOL as the analytic database engine to power your Tableau server and client, means that you will be able to accelerate your reporting and visualizations dramatically by boosting your Tableau performance.

EXASOL is the world’s fastest in-memory, high-performance (Massive Parallel Processing or MPP), analytic database which is designed for big data analytics. EXASOL makes large volumes of data analysis in real-time possible, which helps you to accelerate your Business Intelligence and Analytics applications. EXASOL is ideally suited for real-time big data reporting, analysis, and advanced analytics.

EXASOL also enables in database analytics. It supports R, Python, Java, Lua or your preferred programming language to build analytic applications to unearth key insights.

Event Information:

Venue : Webinar (details will be provided after registration)

Dates / Times : You can select any of the 2 time-slots below:

• August 4th 2017 10:00 – 11:00 AM Singapore Time

• August 18th 2017 10:00 – 11:00 PM Singapore Time

About Infolytics Global

The founders of Infolytics Global Pte Ltd (Infolytics) have experience in providing Big Data Analytics and Data Visualization solutions for all sizes of companies for over 15 years. With our vast knowledge and experience in Data Warehousing and Business Intelligence we have the capability for being the “go to” company for BI solutions. Our expertise is on delivering insights using data visualisation and Big Data Analytics.

We have vast experience in end to end project management of leading BI software like Tableau, QlikView and Qlik Sense. Our team has been part of some of the largest Tableau and QlikView deployments in Asia. One such example is a large semiconductor manufacturing company with more than 3,000 BI users globally. 

What is Exasol?

Data analytics on very large sets of data can be frustrating in terms of performance. Many Business Intelligence applications like Tableau can annoyingly get slow when you are trying to perform real time analysis on large data sets. Solution is usually consolidating and thus reducing the size of the data but this requires extra development and scripts to maintain plus, you lose the granularity of the data.

A good solution to this problem is EXASOL, worlds fastest in-memory, high-performance (Massive Parallel Processing or MPP), analytic database which is designed for big data analytics. EXASOL makes large volumes of data analysis in rel-time possible, which helps you to accelerate your Business Intelligence and Analytics applications. EXASOL is ideally suited for real-time big data reporting, analysis, and advanced analytics.

EXASOL deploys two techniques together : Massive Parallel Processing (MPP) which enables clusters of hardware running EXASOL to achieve the task and in-memory analytics. The database also deploys columnar data compression and storage.

Like a standard RDBMS, EXASOL uses a standard SQL interfaces avoiding the trap of NoSQL skill shortages and easy compatibility with pre-existing applications and data structures.

In below video, you can see how EXASOL can speed up Tableau. The example shows how data residing in two large tables (2.5 billion and 24 billion rows) can be analyzed in Tableau. Tableau has a connector to EXASOL.



EXASOLs in-memory technology enables large amounts of data to be processed in RAM which is significantly faster than processing data residing on hard disk. EXASOL also deploys column-based storage and compression to reduce the number of I/O operations and amount of data needed for processing in main memory and accelerates analytical performance. And Massively Parallel Processing (MPP) enables queries to be distributed across all nodes in a cluster using optimized, parallel algorithms that process data locally in each node’s main memory. The EXASOL intelligent database is self-optimizing and tuning-free. It gives you more time to focus on analytics and insights, not administration.

EXASOL also enables in database analytics. It supports R, Python, Java, Lua or your preferred programming language to build analytic applications to unearth key insights.

Dramatically speed up your Tableau visualizations with EXASOL

Tableau is one of the best Business Intelligence and Data Visualization software in the market today. Its game changing drag-and-drop, self-service experience is unparalleled and it is popularly used worldwide.

Tableau is highly optimized and works quite fast in many applications but in applications where you need to read large volumes of data, like hundreds of millions or billions of rows, it can get dramatically slow (yes there are some illustrations of Tableau with large datasets but they are very isolated, single report demos and in a real life dashboard things can get pretty slow if you need to reaf more than 100 million rows).

Tableau is aware of this problem so back in March 2016, they have purchased HyPer, a high performance database system initially developed as a research project at the Technical University of Munich (TUM). Unfortunately, they did not release any plan for the availability of HyPer yet.

Luckily, there is already a solution out there, more established and powerful than HyPer. The solution is Exasol.

EXASOL analytic database management software is currently the fastest, in-memory analytic database in the world. Since 2008 EXASOL led the Transaction Processing Performance Council's TPC-H benchmark for analytical scenarios, in all data volume-based categories 100 GB, 300 GB, 1 TB, 3 TB, 10 TB, 30 TB and 100 TB.

If you are using Tableau’s great BI, reporting and visualization software to see and understand your data, but are struggling with performance, then you need EXASOL. Using EXASOL as the analytic engine to power your Tableau front-end tool means that you will be able to accelerate your reporting and visualizations dramatically.

You can see Exasol in action below. Note that, Tableau already has a native connection to Exasol.

Exasol does one thing and one thing extremely well. Its high-performance, in-memory, MPP database is specifically designed for in-memory analytics. Exasol analytic database achieves lightning-fast performance with linear scalability by combining in-memory technology, columnar compression and storage, and massively parallel processing.

Since 2014, EXASOL has maintained its position as the undisputed leader in TPC-H benchmarks.  From data volumes that range from 100GB right up to 100TB, EXASOL holds the number one position - by a significant margin - over other solutions, for both raw performance and price-performance.

Exasol also offers out-of-the-box support for R, Python, Java and Lua. EXASOL also allows you to integrate the analytics programming language of your choice and use it for in-database analytics. It can easily connect to your existing SQL-based BI, reporting and data integration tools via ODBC, JDBC, .NET as well as a JSON-based web socket API.

Tableau has acquired HyPer high performance database system

Tableau has announced the acquisition of HyPer, in-memory, high performance database system designed for simultaneous, high performance OLTP and OLAP processing.

Hyper high-performance database system will be integrated into Tableau’s product offerings and bring a host of new capabilities to Tableau customers such as faster analysis of large data sizes, richer analytics, enhanced data integration and data transformation as well as support for semi-structured and unstructured data.

Tableau is highly optimized and works quite fast in many applications but in applications where you need to read large volumes of data, like hundreds of millions or billions of rows, it can get dramatically slow (yes there are some illustrations of Tableau with large datasets but they are very isolated, single report demos and in a real life dashboard things can get pretty slow if you need to reaf more than 100 million rows). There are some other tools also available in the market with native Tableau data connections. Most notable and famous of these is EXASOL in-memory analytic database management software.

Like Tableau, HyPer grew out of a research project. started in 2010 by professors Dr. Thomas Neumann and Dr. Alfons Kemper, chair of at the Technical University of Munich (TUM) Database Group. Four of the project’s Ph.D. students, Tobias Muehlbauer, Wolf Roediger, Viktor Leis and Jan Finis, will join the Tableau family, focused on integrating Hyper into Tableau products.[1]

Here is a detailed definition of Tableau's new HyPer in the project website :

"HyPer is a main-memory-based relational DBMS for mixed OLTP and OLAP workloads. It is a so-called all-in-one New-SQL database system that entirely deviates from classical disk-based DBMS architectures by introducing many innovative ideas including machine code generation for data-centric query processing and multi-version concurrency control, leading to exceptional performance. HyPer’s OLTP throughput is comparable or superior to dedicated transaction processing systems and its OLAP performance matches the best query processing engines — however, HyPer achieves this OLTP and OLAP performance simultaneously on the same database state. Current research focuses on extending HyPer’s functionality beyond OLTP and OLAP processing to exploratory workflows that are deeply integrated into the database kernel by utilizing HyPer’s pioneering compilation infrastructure."[2]

[1] - Welcome, Hyper team, to the Tableau community!
[2] - http://hyper-db.com/