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 all aware of the fluid data sphere, and of the speed that new approaches, technologies and solutions are showing up every day. At the same time, there is an incredible list of companies that benefited the most of their data. While trying to make it to this amazing list, there are variety of obstructions, challenges and potential problems, that can easily shift the focus of the project in some direction that is not at all related to project goals.
In situations like this, it can be really helpful to work with someone who faces with this challenges every day. 🙂
The goals that we set for us when starting a new project, is to deliver the following benefits to the other party:
Experienced Experts. Doing almost any job is easier if you have experience with it. The situation is the same when it comes to data projects. Developing a data product with the experts who already worked on similar projects can benefit in terms of the experience that can be used to find better answers to some challenges, make better architecture decisions and save more time in different phases of the project.
Knowledge Transfer. When it comes to the development of the data product, this phase is essential. Data Science project makes sense only if the whole relevant points within an organization understand it. Only then the organization can be sure that the data project is successful, since only then the solution can be constantly improved.
Focus on Business Needs. Working on a data product can be very exhaustive, and can require a lot of time. The user of the final product must be involved in the process of sharing experience and know how, planning and defining goals and product testing. But in all of the other times, it is usually required to focus on your business needs, and leave the technical stuff to the ones who are specialized in this field.
Flexibility. The most important characteristic that we are trying to preserve at our company is to stay flexible and agile in the work we are doing. Because the whole data field by itself requires flexibility to gain the most benefits from the data product. Working with team of experience data experts should deliver the same benefit to the organization, and the final product should be easily improved, scaled and integrated.
Technologies we use to get the most out of data.
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.
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.