This document provides information about the ICT cluster in St. Petersburg, Russia. It details that the ICT cluster includes over 300 companies and exports over $750 million annually. Some of the key industries include software and IT services, with over 17,000 software developers in the talent pool. The document also lists several major international R&D centers and top ICT companies located in St. Petersburg and provides an overview of the supporting institutions and infrastructure for ICT entrepreneurs and startups in the region.
The First St. Petersburg Business Incubator was launched in 2007 to support innovative small and medium enterprises in St. Petersburg. It provides office space, equipment, services, and financing assistance to resident companies. Over time, the business incubator has supported over 60 companies and led to increased revenues, employment, and tax payments in St. Petersburg. It aims to further expand international cooperation and educational programs to help businesses in the incubator continue to succeed.
The document is a confidential questionnaire for gathering a client's basic financial situation. It requests information about family status, occupation/income, mortgages, real estate, savings, investments, and other assets. Providing this information and accompanying documents will help ensure the best use of time during a financial interview and allow for a discussion of appropriate options given the client's specific circumstances.
The document discusses different topics related to software development processes and tools. It provides information about Scrum methodology and roles like Product Manager and development teams. It also talks about version control tools like SVN and continuous integration tools like Hudson. Various software development concepts are explained like trunk-based development, feature flags, and deploying features to production in phases. Overall workflows involving coding, code reviews, testing and deploying software updates are described.
This document summarizes new features in Spring 3 and 3.1 for component-based application design. Spring 3 focuses on annotation-based components while also supporting concise XML configurations. Key features include stereotypes, factory methods, expression language support, standardized annotations, validation, formatting, scheduling, and REST support. Spring 3.1 enhances environments with profiles for bean definitions, enables Java-based configuration, adds a "c:" namespace for XML, and introduces declarative caching capabilities.
The document discusses Netflix's cloud architecture on Amazon Web Services (AWS). It aims to be faster, scalable, available and allow developers to work more productively. Some key points are moving from a central SQL database to distributed NoSQL stores, replacing sticky in-memory sessions with a shared cache, and optimizing for latency tolerance over chatty protocols. The architecture also focuses on layered service interfaces over tangled code and instrumenting services rather than code.
This document discusses Google's infrastructure and data centers. It describes Google's use of large data centers containing thousands of servers and petabytes of storage. It also summarizes Google's development of technologies like GFS, MapReduce, and BigTable to handle massive amounts of data across their infrastructure. Key details are provided on hardware specifications, network switches, reliability targets, and the engineers involved in developing Google's data-handling systems.
Netflix uses cloud computing to address challenges in scaling its infrastructure to support unpredictable growth. It has transitioned its website to be nearly 100% cloud-based using Amazon Web Services (AWS) to gain the scale, availability and agility needed. AWS provides tools and features like auto-scaling that allow Netflix to easily expand capacity as its subscriber base grows by over 50% per year. By leveraging AWS' mature cloud platform, Netflix can focus on its core video business rather than managing data centers.
This document summarizes a talk about Facebook's use of HBase for messaging data. It discusses how Facebook migrated data from MySQL to HBase to store metadata, search indexes, and small messages in HBase for improved scalability. It also outlines performance improvements made to HBase, such as for compactions and reads, and future plans such as cross-datacenter replication and running HBase in a multi-tenant environment.
The document discusses domain-driven design and modeling complex domains. It provides an example of modeling a shipping domain to understand cargo routing. Entities in the domain include Cargo, Itinerary, and Leg. A Cargo has an origin and destination. An Itinerary is generated by a Routing Service and consists of a series of Legs, where each Leg specifies a load and unload location for the Cargo. Modeling these concepts helps address routing needs like booking or rerouting shipments.
This document discusses how to create "big agility" by focusing on goals and outcomes rather than processes. It advocates questioning assumptions and continuously learning through experiments. Key points discussed include developing personas and story maps to understand users' needs, planning iterations to balance discovery, delivery and learning, and measuring real value delivered rather than effort spent. Cross-team collaboration and creating a shared understanding of what success means for stakeholders is also emphasized. The document provides examples of tools and practices for building agility within, across, and outside of teams.