Whether you are dealing with some challenges in data manipulation, you are unclear what the best possible approach to some challenge would be, or you want to hear an opinion based on experience from similar projects and challenges, we are more than glad to assist you.


We are excited to use our technical skills and agility to help you build your data product, and put the data into work. We will plan, implement and deploy the solution to your challenges, that will help you grow your business.


Want to learn more about these data concepts? Whether your technical team of professionals wants to improve their skill set and hands-on knowledge, or your leadership team wants to keep up with the approaches in data driven organizations, we can organize the training that fits your specific needs

How we can help you?


We are eager to help in getting to actionable insights from your data, by delivering the following benefits:

Experienced Experts. Expertise from similar projects is benefiting to the company in terms of better addressing of business needs, faster time to the insights, time savings in different phases of the project and faster integration of the data pipeline with the business processes.
Knowledge Transfer. We are highly devoted to transferring the relevant know how in the scope of advanced analytics project, in order to enable the proper understanding of the insights and the impact of all the relevant stakeholders within organization, for a better decision making.
Focus on Business Needs. The essence of any successful (analytics) project is the focus on relevant business needs and processes. By providing a deep technical expertise in the domain of advanced analytics, we are enabling our clients to focus on the business and data driven decision making.
Flexibility. In order to benefit from advanced analytics, it is of great importance to dynamically make, test and accept the hypothesis and assumptions in a certain domain, based on data driven insights. By working in a flexible and agile manner, we will enable easy and fast analytics, fast deployment, constant improvements, scalability and seamless integration.


Spark is a fast and general processing engine. It makes data processing very fast on large volumes and cheap at the same time. It is an open source tool, and probably the most popular one for large scale data.
Hadoop is an open source framework for storing and processing large volumes of data. It makes storing massive amounts of data cheap, and easy to manipulate, process and analyze at the same time.
Databricks makes running Spark easy, and it significantly improves our productivity. It makes Spark deployment and cluster management their job, so we can easily focus on our business logic, needs, and data.
Elastic provides a great toolset to easily access, search, explore and visualize various data types. Full text search, log analysis and visualising data has never been easier. It really stands off when combined with other tools.
SAS is a software suite for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. It provides a high quality enterprise software, with more than 40 years of experience with data.
AWS stack of technologies provides various tools to build high quality data applications in cloud based environment, with increased security policies. Using this toolset, we can focus directly on our challenges, and spend less time on technical issues.
Python is one of most popular programming languages for Data Science. It makes development fast and cheap. With considerable number of libraries it can efficiently answer to most data challenges, plus it is easy to learn.
Django offers a complete high-level web framework that encourages rapid development and clean, pragmatic design. It is well suited to build custom platform managers, control centers and custom reports and dashboards.
Sklearn and Pandas are top of the shelf Python libraries for data science. Pandas provides rich data structures to represent the data, and aggregate on variety of functions. Once we know the data, we can train machine learning models with Sklearn.
Tableau is software that allows us to visually explore our data and make beautiful dashboards and reports. It is really convenient to display reports on mobile devices, so we can have our analytics in our pocket at any time.
Apache Hive is a Data Warehouse tool for Hadoop stack. The general purpose of Hive is to represent various data structures and types as table structure, query and analyze the tables using standard SQL queries, and interact with relational databases.
Amazon Redshift is a Data Warehouse tool that is hosted on Amazon Web Services Platform. It is able to handle analytics workloads of large scale datasets. It is a really convenient tool to use for analytics on structured data in cloud environment.
Relational Databases are an important part of most data solutions and platforms. They are very well optimized for persistence and analytics of the structured datasets. Most reporting systems are built on top of relational databases.
NoSQL technologies are convenient in use cases when the structure of the data is not relational, or not very rigid. We use them often for the realtime web applications, since they are usually capable to handle large workloads.