As the amount of data continues to skyrocket, organizations using traditional technology platforms are not able to easily process it. The key to our success is to determine how big data fits into your overall business plan. With an effective method for capturing, storing and managing underlying data, big data offers a number of benefits to organizations:
We are ahead of the curve with big data and can assist organizations with their big data initiatives. Our approach has evolved to develop the industry’s best practices and techniques. Our overall strategy guides organizations through an interdisciplinary approach complemented by our advanced analytic solutions that maximize ROI. Our services are designed to assist organizations at every step:
Big data offers organizations new ways to utilize data and extend their competitive advantage. With a wide range of technologies and distributors, many organizations are unsure of how and where to start with a big data strategy. Our readiness assessment and conceptualization for big data is designed to answer your questions and help you identify where you will benefit and what steps to take to gain insights and value so you can begin using big data and related technologies. Our team can assist you with creating specific use cases, business objectives, ROI models and an implementation roadmap.
Our PoC Pilot program helps your organization put use-case specific vendor agnostic big data implementations into place. These pilot programs allow you to see big data in action, helping you make crucial decisions on rollouts or expansions. We can help you plan the entire implementation, build infrastructure plans, help design clusters based on needs and provide team training.
Our extensive experience working with big data technologies have resulted in successful implementation of solutions in many industries and across many Hadoop distributions such as Apache, Cloudera, Hortonworks and MapR.
We collaborate with IT and key business stakeholders to reach milestones for implementation. Our infrastructure advisory and planning services are based on the following factors: Access: Big data centric solutions store business critical data including sensitive information about the customer, their financials and marketing, that are strictly governed by corporate security and compliance policies. It is imperative that these types of businesses have access to big data, and the entire activity should be audited to satisfy regulatory compliance requirements. We understand these policies and incorporate different levels of access and control. Capacity: Our systems are designed to handle peta or exabytes and can be scaled seamlessly to fit customer needs. “Scale-Out Clustered Architecture” has the ability to add capacity in modules or arrays transparent to users without taking the system down. Since nodes of storage capacity with embedded processing power and connectivity can grow seamlessly, this architecture avoids the silos of storage utilized by traditional systems. A key advantage for deploying big data is to manage large numbers of files in different formats (logs, audio, video etc.). Managing these files reduces scalability and impacts performance. We take these factors into consideration during the planning phase and design an architecture to handle these specific situations. By leveraging object-based storage architecture,
big data storage systems can expand file counts into the billions without suffering overhead problems. Object-based storage systems can also scale geographically, enabling large infrastructures to be spread across multiple locations. To be able to harness the true power of distributed computing, it is essential to have the right cluster to support it. There are multiple factors to consider in order to build the correct organization specific cluster, such as:
Security: Big data can be run in a secure mode by leveraging your existing identity management infrastructure such as Active Directory, LDAP etc. This approach minimizes cost by eliminating third party solutions and uses existing skills to achieve the following:
Latency: We make a difference in planning your big data infrastructure by understanding your key business goals and revenue drivers.
We will design a system with ideal performance and reduce the latency factor for real-time applications. As an early adopter of Apache Spark, we are also a certified systems integrator and trainer. We can leverage the in-memory distributing computing capabilities of Spark for real-time streaming and analytics. This is exactly why we utilize a “Scale –Out Architecture,” so we can quickly enable the cluster of storage nodes to increase processing power. Cost: We understand the dynamics around controlling cost from start to finish. Our approach starts by defining the case, and then developing a plan to execute the project in a POC mode. This can be achieved in a variety of ways: Utilizing unused hardware to form a cluster for the POC Utilizing the open source software for the POC without having to invest in a specific big data distribution Developing the project to realize the business value from big data We start with a “Proof of Technology,” to evaluate various big data distributions based on goals, vision and cost considerations. Depending on specific big data technology or tools, we design a “Scale-Out Architecture” model without using an expensive hardware configuration upfront. Finally, we design a training strategy for organizations based on existing internal IT teams for skills adoption.
Data Ingestion & Streaming: We know the challenges that exist in order to load data into a cluster for analysis. It comes from multiple sources and in a number of forms – including structured and unstructured data. Big data overcomes these challenges through quicker data analysis compared to conventional RDBMS systems, and has the power to analyze data that these systems cannot. Most importantly, it easily integrates with real-time streaming sources. We have extensive experience with these data loading projects and have developed a list of best practices: 1. Identify required data points 2. Identify data sources and the right drivers 3. Data source connectivity 4. Configure and set up the right tools (Ex: Sqoop, Flume, Kafka, Storm) to use the identified data sources
5. Develop scripts for the tools for automated data loading 6. Manage work flow for the data loads 7. Schedule and monitoring data load jobs 8. Performa data quality checks We will customize this experience to your requirements and utilize various big data related tools, such as Sqoop, Flume, Kafka, and integrate ETL tools (Informatica, Pentaho, Talend) for the data loading process. Data Processing: Several techniques exist to process big data, including batch-oriented (most common) and in-stream processing. As big data technologies have evolved, it is necessary to use real-time query processing and in-stream processing for many business applications. We have a deep understanding of data processing and
developed a hybrid architecture to can effectively process data in batch mode and in real time (Spark, Storm). This phase involves the implementation and development of algorithms and scripts. Efficient programs (MapReduce, customized Hive and Pig Latin scripts that can crunch data either for further analysis, Custom User Defined Functions (UDFs) and reusable scripts will be developed. We have a team of certified developers that are proficient in various object-oriented programming languages and have years of experience writing efficient code. We have success not only in developing these programs and scripts, but also in performance enhancement. We schedule and monitor jobs and collect statistics to help us gain a better insight for potential areas of improvement. The diagram below illustrates a high level big architecture of our typical implementation:
We are an elite group of data engineers, scientists and problem solvers who share a passion for solving complex problems and finding valuable insights. This passion enables our team to collaborate and deliver high-quality solutions to our clients. We have been in business for more than a decade, working with hundreds of clients in multiple industries. Our goal is to provide our clients with solutions that solve specific problems or increase overall ROI. Our focus is on using the right tools and techniques to deliver value to your business. Our customers are able to explore, predict and learn from their data.
As early adopters of Apache Spark, we also invested in advanced research around the program. We are developing a cognitive framework using Spark and Lamba Architecture. Organizations today are broadly adopting predictive analytics and building models ranging into the thousands. The ratio of models being utilized offline versus real-time is approximately 50 percent, and increasing rapidly. There are forecasts that soon, two-thirds of all predictive models will cater to real-time decision making such as fraudulent prevention, etc. This makes it increasingly more important to develop an architecture
that can bring predictive analysis to the decision makers Spark offers standardized access to data across the organization and implements a repeatable industrial-scale process for production, but the program is missing a component to visually represent model predictions. The third party apps that leverage Spark’s model of data processing introduce latency into the system. To overcome this, we designed a framework that seamlessly integrates with Spark. The framework leverages the reactive platform offered by Type Safe, the company behind
the popular Scala programming language and utilizes Activator and Play frameworks. Type safe Activator and Play frameworks, which are Scala-compliant, work effortlessly with Spark’s processing framework. The front-end visual architecture is plug-and-play. When used in conjunction with the most popular visualizations frameworks ranging from angular.js to D3, it increases the library of advanced visualizations. The whole framework exploits akka message-driven runtime and is entirely distributed and resilient with scalability across the entire stack.