Tag: Big Data

Community In The Time Of Covid

September 2, 2021

Qbeast turned one year old, and what a year it has been.

Yes, you read that right. We decided to start a company in 2020, in the middle of a pandemic. Call us crazy, but with patience and determination anything is possible. Qbeast stands today because of the hard work of all the employees, and as a company we show our people how much we appreciate them.

We always look for new ways to bring our people together, through having fun, through learning, and through trusting each other. Giving people the ability to interact during the pandemic has been a challenge that as a company we have met and succeeded through frequent video meetings. These calls created fun and lasting memories while at the same time working toward a common goal together.

But after being holed up inside for months, away from co-workers and others, our first attempt was attending the Mobile World Congress event in Barcelona. As part of the 4YFN we had a booth with Barcelona Activa and with Banco Sabadell Bstartup. After all, people miss networking and seeing each other, and if there is enough support in attending an event being held as safely as possible in person, then why not try?

But how do you promote that team spirit when everyone is remote you ask? Last month we organized a virtual team building event with the help of our friends from Let’s Cook, a meal kit service in Barcelona, where you select a meal plan, receive the recipes and exact ingredients at your doorstep, and prepare your meals with step-by-step instructions and videos.

We all connected virtually in order to cook together and then enjoy a homemade lunch offered by Qbeast. This was an opportunity for the team to socialize and just enjoy having some fun. Of course, for some of us the vietnamese rolls we prepared looked more like ping pong balls in the end, but still delicious!

With vaccines rolling out, companies project that things will be getting somewhat back to normal by this fall. Being social is human nature, and people will always crave that face-to-face contact for personal connections.

Through all of this, we should not lose the importance of human connection. Our workplace is more than getting the job done. It is also a place to feed the soul, nurture relationships and create opportunities for people to engage and interact.

Zoom, Google Meets, etc., have been an invaluable tool to help the world continue to communicate. Ultimately, it is the human connection that will get us through this pandemic and sustain us as we collectively build the new normal in our workplaces.

Team building is a celebration, and we will continue to celebrate together.

I-BiDaaS Project with Qbeast Completed With Great Success

Friday, March 26, 2021

The Barcelona Supercomputing Center(BSC) was a member of the I-BiDaaS consortium, and Qbeast’s solution took part in the I-BiDaaS platform as BSC’s contribution. Qbeast’s visualization tool Qviz, which will be part of the Qbeast Platform product, was used in the banking use cases(CaixaBank) that were conducted during the project.

Industrial-Driven Big Data as a Self-Service Solution (I-BiDaaS) an EU-funded project that aimed to encourage IT and non-IT big data experts to easily apply and collaborate with big data technologies by developing, creating, and demonstrating a unified solution that significantly increases data analysis speed while coping with the pace of data asset development, and promotes cross-domain data-flow towards a thriving data-driven EU economy.

The vision of I-BiDaaS was to shift the power balance within an organization, improving efficiency, lowering costs, generating greater employee empowerment, and increasing profitability. To create a stable environment for methodological big data exploration in order to develop new products, services, and technologies. To build innovations that will boost the productivity and competitiveness of all EU companies and organizations that deal with large, complex data sets.

The I-BiDaaS project was successfully launched in January, 2018 with 13 participating organizations from 8 different countries and the duration of the project lasted for 36 months.

Qbeast, as part of the I-BiDaaS tools, was tested and analyzed in the context of fraud detection in the use cases of advanced analysis of bank transfer payment in financial terminal and enhance control of customers to online banking. Qbeast was credited with a 30% reduction in data processing time and a potential cost reduction in commercial data analytics solutions licenses.

CaixaBank concluded: “the most important conclusion of the use case was the ability to perform big data clustering analytics in a very agile way, based on existing or custom-tailored clustering algorithms.

I-BiDaaS tools were validated for the full cycle of big data processing, as a self-service for non-IT and intermediate users, with advanced users able to customize their big-data analysis.

“One of the key gains Qbeast has obtained from the I-BiDaaS project is clearly the close contact we have had with the industry. Having CaixaBank, Telefónica I+D and Centro Ricerche Fiat in the project, and being able to work with them so closely, has had a paramount impact on how Qbeast is now, and how Qbeast will be shaped in the future.” said Raül Sirvent, Principal Investigator for BSC during the I-BiDaaS project and Senior Researcher at the Department of Computer Science, BSC.

For further information on I-BiDaaS, please visit the I-BiDaaS website.

About Qbeast

Qbeast is here to simplify the lives of the Data Engineers and make Data Scientists more agile with fast queries and interactive visualizations. For more information, visit www.qbeast.io

© 2020 Qbeast. All rights reserved.

Qbeast taking part in IESE’s MBA BTTG program

Monday, March 01, 2021

Qbeast was chosen to participate in the Barcelona Technology Transfer Group (BTTG) program, an initiative of IESE Business School that MBA students introduced in 2016. According to The Economist’s WhichMBA? 2021 Full-Time List, IESE’s MBA program has been ranked as the best MBA program in the world.

BTTG’s objective is to create a platform that connects inventors from research and development labs with the business acumen and talent they need in order to bring their product successfully to market.

The goal of the program is to help promote the market entry of creative new technologies, giving the greatest potential for sustainable growth and therefore have a positive impact on society and technological development. It also gives MBA students a first-hand experience of working in a start-up environment that offers invaluable training experience and facilitates relationships between IESE, it’s MBA students and the local startup ecosystem.

The program is a three-month collaboration between Qbeast and an IESE appointed team, including a group of five MBA students who provide the company with quality work while producing several ideas, and a project leader responsible for task planning and project management. The students partner up with the Qbeast team to assist with the business strategy, define market fit and segmentation and determine suitable commercialization models and sales channels.

“The idea: put scientists and MBA students in a room together, and see what happens. The answer has been that a lot happens.” said Luca Venza, founder of the BTTG and Director of Technology Innovation, Transfer and Acceleration, IESE. “The program is proof that business and science can coexist successfully, if the conditions are right and the mutual respect is there.”

For further information on BTTG, please visit the BTTG website.

About Qbeast

Qbeast is here to simplify the lives of the Data Engineers and make Data Scientists more agile with fast queries and interactive visualizations. For more information, visit www.qbeast.io

© 2020 Qbeast. All rights reserved.

How Qbeast solves the pain chain of Big Data analytics

Are you ready to find out how speeding up data analysis by up to 100x solves data teams’ pain points?

Well, first let me give you some background information. According to a survey conducted by Ascend.io and published in July 2020, 97% of data teams are above or at work capacity.¹ Given that every day more and more data is generated and stored, this is not good news for data teams and organizations. Yet, the capability to leverage data in business has never been more critical.

The pain chain

The survey states that the ability to meet data needs is significantly impacted by slow iteration cycles in data teams. This aligns with the feedback that we received from our customers’ data teams as well.

To explain why iteration cycles are slow, let’s use the concept of the pain chain. The pain chain was first introduced by Keith M. Eades and is a map to describe a sequence of problems in an organization’s process.² The pain of one role in the company causes the pain of another function. In our case, the data pain chain starts with the Data Engineer, follows to the Data Scientist, and finally involves the decision-makers. To keep in mind, the data engineer is the one who prepares the data. The data scientist uses this data to create valuable and actionable insights. And well, the decision-maker is a project manager, for example, who wants to get a data-driven project done.

The survey found that data scientists are the most impacted by the dependency on others, such as data engineers, to access the data and the systems (48%). On the other hand, data engineers spend most of their time maintaining existing and legacy systems (54%).

How does this impact the decision-maker? Well, it leads to a significant loss of value due to delayed implementation of data products or because they cannot be implemented at all.

How do we solve it

Qbeast’s solution tackles the pain chain on several fronts to eliminate it altogether.

Front 1: Data Engineering

There is nothing more time consuming and nerve-racking than maintaining and building complex ETL pipelines.

Less complexity and more flexibility with an innovative storage architecture

Can’t we just work without ETL pipelines? You may say yes, we can use a data lake instead of a data warehouse. We can keep all the data in the data lake and query it directly from there. The downside? Querying is slow and processing all the data is expensive. But what if you could query all the data directly without sacrificing speed and cost?

With Qbeast, you can store all the data in your data lake. We organize the data so that you can find what exactly you are looking for. Even better, we can answer queries by reading only a small sample of the dataset. And you can use your favorite programming languages, be it Scala, Java, Python, or R.

How do we do this? With our storage technology, we combine multidimensional indexing and statistical sampling. Check out this scientific paper³ to find out more.

Our technology’s advantage is that we can offer superior query speed than data warehouses while keeping the data lakes’ flexibility. No ETL pipelines but fast and cost-effective. The best of both worlds, so to speak.

Front 2: Data Science

We know that if you are a data scientist, you do not care so much about pipelines. You want to get all the data you need to tune your model. And it is a pain to rely on a data engineer every time you need to query a large dataset. You are losing time, and you can’t focus on the things that matter. But what if you could decide the time required to run your query yourself?

Data Leverage

By analyzing the data with a tolerance limit, you can decide how long to wait for a query and adjust the precision to your use case. Yes, this means that you can run a query on whatever you want. Do you want to know the number of sales in the last months? Full precision! But do you really need to scan your whole data lake to see the percentage of male users? Probably not.

With Qbeast, you can get the results you need while accessing only a minimum amount of available data. We call this concept Data Leverage. With this option, you can speed up queries by up to 100x compared to running state-of-the-art query engines such as Apache Spark.

Conclusion

A storage system, which unites multidimensional indexing techniques and statistical sampling, solves the data analytics pain chain by speeding up queries, reducing complexity, and adding flexibility. This results in a significant speed-up of iteration cycles in data teams. Increased productivity and speed of data analysis itself have a colossal impact on the ability to meet data needs and to create superior data products. And above all, alleviating the pain chain results in a happy data team, decision-makers, and customers.

But the pain chain doesn’t end here! Now it is time for the application developers to pick up all the insights uncovered by the data scientists and use them to build amazing products! That’s a topic for another post, but I bet you have guessed; we have a solution for that too.

References

1. Team Ascend. “New Research Reveals 97% of Data Teams Are at or Over Capacity”, Ascend.io, 23 July 2020, www.ascend.io/news/company-announcements/new-research-reveals-97-of-data-teams-are-at-or-over-capacity. Accessed 28 December 2020.

2. Eades, Keith M., The New Solution Selling: The Revolutionary Sales Process That is Changing the Way People Sell, McGraw-Hill, 2004.

3. C. Cugnasco et al., “The OTree: Multidimensional Indexing with efficient data Sampling for HPC,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 433–440, doi: 10.1109/BigData47090.2019.9006121.

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