Scalable Data Architectures

Design an architecture that implements your strategy. It matches your evolving needs and allows efficient flow and treatment of your crucial assets - data.

Data Analytics

Machine learning and analytics. Understand cross-relations and discover trends. Automatize.

Connecting the dots

Take the work coordination to the next level. Empower your organisation with an Enterprise Knowledge Graph. Capture the knowledge of your organisation in a system which stores the relations between different projects and initiatives across the organisational structure.

About Scaleia

  • People always search for technologies that allow them to make great things. Only some of these technologies would scale efficiently with the growing needs. Most of them can hold you back. In the times of constant change, IT strategy has become the key factor for organisations to succeed.
    Scaleia is about finding data strategies that are scalable.

  • We focus on systems architecture as well as strategic planning software for meaningful bussineses.

  • Our experience from R&D (CERN) as well as the energy and financial sectors includes systems architecture, AI, big data analytics, stream mining, search engines and knowledge management.

  • Independent in looking for efficient solutions which leverage the power of the same open source software that powers the most disruptive Internet companies.

Team

Jan Iwaszkiewicz (jan @ scaleia.com)

Founder, data architect. Jan is passionate about understanding clients' needs and designing innovative yet robust solutions With his combined computer science and physics background, Jan worked at CERN on data analytics and information management in two teams: ROOT and Invenio. Later he continued on a number of projects for start-ups, the swiss utility industry, trading, HR and blockchain.


Data Science: Real-World Use Cases

Part of what makes Hadoop and other Big Data technologies and approaches so compelling is that they allow enterprises to find answers to questions they didn’t even ask. This can result in insights that lead to new product ideas or help identify ways to improve operational efficiencies. Still, there are a number of already identified Data Science use-cases, both for Web giants like Google, Facebook and LinkedIn and for the more traditional enterprise. They include:

Recommendation Engine: Web properties and online retailers use Hadoop to match and recommend users to one another or to products and services based on analysis of user profile and behavioral data. LinkedIn uses this approach to power its “People You May Know” feature, while Amazon uses it to suggest related products for purchase to online consumers.

Sentiment Analysis: Used in conjunction with Hadoop, advanced text analytics tools analyze the unstructured text of social media and social networking posts, including Tweets and Facebook posts, to determine the user sentiment related to particular companies, brands or products. Analysis can focus on macro-level sentiment down to individual user sentiment.

Risk Modeling: Financial firms, banks and others use Hadoop and Next Generation Data Warehouses to analyze large volumes of transactional data to determine risk and exposure of fincnaical assets, to prepare for potential “what-if” scenarios based on simulated market behavior, and to score potential clients for risk.

Fraud Detection: Use Big Data techniques to combine customer behavior, historical and transactional data to detect fraudulent activity. Credit card companies, for example, use Big Data technologies to identify transactional behavior that indicates a high likelihood of a stolen card.

Customer Churn Analysis: Enterprises use Hadoop and Big Data technologies to analyse customer behavior data to identify patterns that indicate which customers are most likely to leave for a competing vendor or service. Action can then be taken to save the most profitable of these customers.

Social Graph Analysis: In conjunction with Hadoop and often Next Generation Data Warehousing, social networking data is mined to determine which customers pose the most influence over others inside social networks. This helps enterprises determine which are their “most important” customers, who are not always those that buy the most products or spend the most but those that tend to influence the buying behavior of others the most.

Customer Experience Analytics: Consumer-facing enterprises use Hadoop and related Big Data technologies to integrate data from previously siloed customer interaction channels such as call centers, online chat, Twitter, etc. to gain a complete view of the customer experience. This enables enterprises to understand the impact one customer interaction channel has on another in order to optimize the entire customer lifecycle experience.

Network Monitoring: Hadoop and other Big Data technologies are used to ingest, analyze and display data collected from servers, storage devices and other IT hardware to allow administrators to monitor network activity and diagnose bottlenecks and other issues. This type of analysis can also be applied to other forms of networks, include transportation networks to improve fuel efficiency.

Research And Development: Enterprises, such as pharmaceutical manufacturers, use Hadoop to comb through enormous volumes of text-based research and other historical data to assist in the development of new products.

These are, of course, just a sampling of Data Science use cases. In fact, the most compelling use case at any given enterprise may be as yet undiscovered. Such is the promise of Big Data.

source:Wikibon

Customized proof-of-concept solution can be created in a matter of days.

Selection of software tools used by Scaleia

Hadoop

The most popular big data storage and processing framework. The Apache Hadoop® project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.

Mahout

The Apache Mahout's goal is to build scalable machine learning libraries. Scalable to reasonably large data sets. Mahout's core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm.

Contact

Every organization has different needs. We offer you our 100% independet advise. Email us , and we will be happy to discuss your data management challenges.