Big Data (general concepts)
Big Data Qlik proposition
Big Data Qlik Tech Partners solutions
Big Data competition
Qonnections Key Messages
100
What are the 4 V of Big Data and some examples of each? a. Volume, Value, Vision, Variance b. Variety, Velocity, Vendor, Veracity c. Volume, Variety, Velocity, Veracity d. Valdidentro, Villafranca, Villafiorita, Vistamare
c. Volume, Variety, Velocity, Veracity
100
What is the big challenge that the modern companies are facing when they need to deploy a Big Data solution? We see 4 big challenges: • How can they deliver different experiences to different users? • How can deliver a simple, guided experience to the user? • Is whatever they implement today going to be pertinent in the future What is the last one?
• How can they have one solution address be used in multiple ways to address different situations?
100
According to the Qlik technological offer. Which of the following statement best describe the Big Data Partners? 1. Implementation Partner, which know both the BIG Data Complexity environments and Qlik solutions, so that they can implement the most suitable solution using Qlik technology. 2. Technology Partner, which provide a connection between Qlik and the Big Data Sources and/or the Big Data Source itself. 3. Individuals having huge competences on Big Data technology and BI Solutions, can fairly advise on which BI tool best fit the customer’s needs.
2. Technology Partner, which provide a connection between Qlik and the Big Data Sources and/or the Big Data Source itself.
100
Please, identify in the following list of software vendor Big Data Analytics tools (1) and Big Data Analytics key competitors (2). • Palantir • Splunk • IBM • Google Big Data • Amazon web services • Datameer • Alteryx • PluralSight • Microsoft PowerBI • Tableau
• Palantir --> 1 • Splunk --> 1 • IBM --> 1 • Google Big Data --> 1 • Amazon web services 1 • Datameer --> 1 • Alteryx --> 1 • PluralSight --> 1 • Microsoft PowerBI --> 2 • Tableau --> 2
100
Per ognuno dei tre pillar (Data, Platform, Analitics) elencare almeno due novità della June 2017 Release?
Per Data ---> • On Demand App Generation • Visual data profiling –Binning, ordering, table concatenation • Generic REST Connector on Qlik Sense Cloud: Twitter, Facebook & Google Analytics in Qlik Sense Cloud • SAP Multi-provider connectivity update • Bit.ly, Slack, Google Search Console, OneDrive Platform ---> • Mobile offline • Haylard.jsOpen Source contribution (Data Import Library) • API Governance Policy • Remove need for “central node” and centralize logging • Mobile Iron Enterprise & BlackBerry Dynamics Enterprise Mobility ---> • QlikView application conversion Analytics ---> • New Visualizations –Boxplot, Distribution Chart, Histogram, Waterfall • Modify desktop installation package • Smart Search History • Advanced Analytics Integration –R + Python • Color Intelligence –consistent color to master dimension by value
200
Can Big Data and IoT work together to bring greater value to the company? a. Yes, working in conjunction. Data from Iot devices gives more accurately target and increase efficiency b. No, Big Data and Iot are two incompatible word c. Yes but it’s hard to find the right connector. Iot is a part of Big Data environment, once connected; Data scientist is needed to analyze data. d. Wait I will ask to our presales colleagues. They know always the right answer. They are greatest!
a. Yes, working in conjunction. Data from Iot devices gives more accurately target and increase efficiency
200
What Makes Qlik Unique when we talk about different use cases and scenario?
The Qlik platform drives higher ROI from investments in Big Data technologies by ensuring that the value of big data is not restricted to only the elite data scientists
200
When it comes to the BIG Data in the Cloud, what does Qlik suggest? 1. While loading data, take advantage of week-ends and bank holidays because of the huge amount of data takes a long time to be loaded through a low performance band width. 2. The fiber channel hasn’t really, started in Italy, even if the solution works in the major developed countries, we do not suggest to deal with both in some countries like Italy. 3. We recommend to work with the Qlik Cloud instead of the on premises version, since the data are already there. 4. Some Qlik BIG Data Partner offer their solution in Cloud, Qlik treat them likewise on-premises solutions. 5. There are a plenty of BI vendor which work with Big Data Source in the Cloud. We will focus on the on-premises and leave to others the Cloud space
4. Some Qlik BIG Data Partner offer their solution in Cloud, Qlik treat them likewise on-premises solutions. Google BIG Query and Amazon Redshift (Qlik Partner) offer their solution in Claud
200
How Do Tableau position against large volumes of sparse, different data in a large data lake? 1) Tableau leverage a strong partnership with few strategic big data platform vendors and offer smart data visualization capabilities among sparse and unconsistent data sources. 2) Tableau offer out of the box advanced algorithms to inspect large set of data and provide guided insight against a data lake leveraging all the big data technology platform. 3) Tableau present with such interesting features such as a number of connectors to many big data platforms, out of the box predictive analysis via a Python integration, the option to use key metrics or statistics rather than data relationship, etc
3) Tableau present with such interesting features such as a number of connectors to many big data platforms, out of the box predictive analysis via a Python integration, the option to use key metrics or statistics rather than data relationship, etc.
200
Quali sono le “3 big Ideas” su cui stiamo lavorando/sperimentando?
• Leave data where it is • A true hybrid cloud platform • From business intelligence to augmented intelligence
300
Please associate the right description to Big Data and Business Intelligence. • …………………. uses descriptive statistics with high density data to measure things, detect trends, that is, it uses limited datasets, clean data, and simple templates; • …………………… uses inferential statistics and non-linear system identification concepts to infer laws (regressions, nonlinear relationships, and causal effects) from large data sets and to reveal relationships, dependencies, And perform predictions of results and behaviors, that uses heterogeneous datasets (unrelated to each other), raw data, and complex predictive models.
• BUSINESS INTELLIGENCE uses descriptive statistics with high density data to measure things, detect trends, that is, it uses limited datasets, clean data, and simple templates; • BIG DATA uses inferential statistics and non-linear system identification concepts to infer laws (regressions, nonlinear relationships, and causal effects) from large data sets and to reveal relationships, dependencies, And perform predictions of results and behaviors, that uses heterogeneous datasets (unrelated to each other), raw data, and complex predictive models.
300
What are the three pillar for Big Data at Qlik? • The first one is “Accelerating Big Data ROI”. What are the others two pillars?
• Drive Organizational Analytic Maturity • Future Proof Your Investment
300
Which of the following statement best describes “the Accelerators”? 1. Third party technologies which allows Qlik to read data coming from Big Data Source. They are mandatory to get data from some Big Data Sources. 2. Third party technologies which allows Qlik to perform better, getting data from BIG Data Sources faster. They are not mandatory but strongly suggested. 3. Third party technology which converts unstructured data source to structured data sources. This allow Qlik to work with the unstructured data sources. 4. The number 1 and 3 are both true 5. The number 2 and 3 are both true
4. The number 2
300
How do you position Qlik against Tableau in a Big Data scenario? select the correct answers: 1) Tableau is leader in big data analytics, so leave the competition. 2) From a technical point of view, you can set traps pushing customers to consider common scenarios that involves multiple data sources, since Tableau = SQL, so it still has restrictions around supported data sources, level of detail, master/slave sources and calculations, joins and blends. An author should not need a degree in rocket science to determine which tecnique to use with Qlik, with an easy and engaging smart data load interface. 3) Tableau doesn’t provide API to extend its features; tricky and shiny visualizations effects can demonstrate Qlik values leveraging every data source, including big data platforms. 4) Tableau could present performance issues on large sets of data, and multiple sources will stress it even more. Prospect should tests both vendors in real world data scenarios and large data volumes. 5) Tableau has a relational modeling approach to data, so it doesn’t fit with sparsity and variety of unstructured information, often collected in a data lake.
2) From a technical point of view, you can set traps pushing customers to consider common scenarios that involves multiple data sources, since Tableau = SQL, so it still has restrictions around supported data sources, level of detail, master/slave sources and calculations, joins and blends. An author should not need a degree in rocket science to determine which tecnique to use with Qlik, with an easy and engaging smart data load interface. 4) Tableau could present performance issues on large sets of data, and multiple sources will stress it even more. Prospect should tests both vendors in real world data scenarios and large data volumes.
300
Quale di queste due definizioni è corretta per come Qlik definisce il concetto di “Augmented Intelligence” A. Augmented intelligence is a combination of machine and human centric approaches B. Augmented intelligence is a combination of machine and human intuition
A. Augmented intelligence is a combination of machine and human centric approaches
400
What are the main components and ecosystem of big data? Please select 3 options A 2011 McKinsey Global Institute report characterizes the main components and ecosystem of big data as follows: a) Techniques for analyzing data, such as A/B testing, machine learning and natural language processing b) Hardware: 4 octacore , 3 Tera Ram, 2 proxy c) Big data technologies, like business intelligence, cloud computing and databases d) Proxy in DMZ e) Visualization, such as charts, graphs and other displays of the data f) Mobile apps to analyze data.
a) Techniques for analyzing data, such as A/B testing, machine learning and natural language processing b) Big data technologies, like business intelligence, cloud computing and databases c) Visualization, such as charts, graphs and other displays of the data
400
What are the Qlik methodologies that can address a Big Data scenario? In memory is one of these, but we have others. What about other approaches?
Qlik offers 5 different methods to address Big Data situations. - Qlik In-Memory Approach - Qlik Segmentation & Chaining Approach - Qlik Direct Discovery Approach - On Demand App Generation
400
Is it possible to use Accelerators in combination with Hybrid methodologies such as ODAG or Direct Discovery? 1. No, Hybrid methodologies are not compatible with accelerators, they need to fetch data in a slow controlled way. 2. Yes, it is possible but not recommended for the highest run-time computational cost. 3. It is mandatory if the Big Data Source are unstructured. 4. Yes, it is possible only when the Big Data Vendor is directly involved in the project. 5. None of above are true.
3. It is mandatory if the Big Data Source are unstructured.
400
What sentences applies to the following statement: “Vendor xyz deploy a perfect data browsing tool allowing us to get insight out of our data lake either free quering all the data or automatically presenting patterns, trends and forecasts, while Qlik requires expensive hardware architecture to displace similar features.” 1) Actually the statement is false. Some tools available in the market are built over existing advanced analytics (i.e. Watson Analytics and SPSS). BI tools usually still requires a structured data modeling leveraging SQL derived tecniques who are still not compatible with large no-sql data lake. 2) Query response time is affected by data sources performance unless you completely rely on BI architecture, leveraging cores and memory dedicated to the BI platform. As for BARC survey, Qlik query performance are far above the average and Qlik is reported as a top player.Even though a data lake provide high level response time, concurrency of users and large data sets analisys requires time and that time is wasted if an analyst doesn’t know what to look for. 3) Search algorithms or mining tools on homogeneous class and categories of data are good for skilled data scientists. The many of the users will require prepackaged data models to leverage big data analytics efficiently. Integration with platform (R and Python) and middleware (Jethro, Doolytics, etc.) are relevant Qlik features to address real life needs. 4) Hey, there it is, a flying donkey!
1) Actually the statement is false. Some tools available in the market are built over existing advanced analytics (i.e. Watson Analytics and SPSS). BI tools usually still requires a structured data modeling leveraging SQL derived tecniques who are still not compatible with large no-sql data lake.
400
Negli anni ci sono stati diversi cambi di piattaforme tecnologiche come ad esempio: • Intorno al 2000 il passaggio dai 32 ai 64 Bit per poter scalare sul Hardware • Intorno al 2010 Il passaggio dagli ambienti virtuali al cloud per poter scalare a livello infrastrutturale Qual è, nei prossimi anni, la nuova piattaforma che permetterà di poter ottimizza il carico di lavoro?
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