Big Data

3 Factors to Consider in Your Big Data Infrastructure

Big Data initiative means expansion in hardware provisioning. It allows storage and processing of large volumes of complex data. With Big Data, real-time information is available for businesses to derive proactive decision-making.

There are four complexity factors in Big Data: Volume, Velocity, Variety, and Veracity. The volume of streaming data surge is too much for the traditional technologies. The variety of data formats from different sources adds to the processing complexity.

Big Data requires robust infrastructure for storage, processing and networking resources. Below are some inputs for capacity planning:

  • Volume of data
  • Daily Data Surges
  • Data Retention Period
  • Replication
  • Scaling
  • Number of Data Centers

Before the Big Data implementation, review the capacity blueprint of the organization. Analyze how the Big Data environment can coexist with the existing infrastructure. In particular, check your storage, processing, and network support. Decide on what’s the best implementation based on the goals of the organization.

Storage Provisioning Scaleup

Most organizations already have enough storage for a Big Data initiative. However, this traditional database storage can’t support the complexity of Big Data. It means investing in storage optimized for Big Data solutions.

To support the volume of data, you need to scale your secondary storage. Storage must be enough to accommodate petabytes of data. Be sure to provide many restore points based on your business needs.

Large companies can afford to have hyperscale computing environment. This provisioning allows distributed processing to scale to thousands of servers. It can run frameworks like Hadoop. It utilizes PCIe-based flash storage to reduce latency. But, smaller organizations often use object storage or clustered network-attached-storage (NAS).

Design a Big Data architecture that allows direct-attached commodity storage, NAS or SAN. It is also possible to integrate old data to cheaper cloud-based storage (e.g., Microsoft Azure). Ensure quick restore to lessen downtime impact and data loss. For disaster recovery and backups, cloud storage could be an option.

The Big Data storage architecture must be able to scale out to meet the increasing capacity. The architecture can handle billions of files without performance degradation. It uses parallel file systems across many storage nodes.

Distributed Processing Support

Big Data analytics demands enough processing power to support complex analysis. The architecture must support distributed computing for more efficient data processing. It involves complex event processing, visualization, and data mining.

Big Data differs from data warehousing because of its distributed and real-time processing. There is a distribution of compute-intensive workload to small systems running in parallel. Each node usually has two processors and several disks connected in Ethernet. Each cluster provides computing power and storage capacity to perform data analysis.

To achieve efficiency, design servers with Big Data in mind. Servers not optimized to handle a large volume of data can’t meet the requirements. Some organizations opt for on-demand processing in the cloud when the need arises. For their daily workloads, they rely on their on-premise resources.

The sophisticated analytics will become common in both midsize and large companies. The valuable insights from analytics could make a difference in business directions. The infrastructure must be scalable and resilient enough to optimize performance. Unlike the traditional database, there is data shuffling during distributed processing.

Network Upgrade

The emergence of Big Data is more than deploying a new software technology like Hadoop. The volume of data transport in a Big Data requires robust networking hardware. Some organizations jump-start their big data initiative built on their existing resources. The ones operating in 10-gigabit connections only require minor modifications.

Big Data environment must coexist with the existing transaction-oriented RDBMS system. Optimize your network to provide a strong foundation for volume, velocity, and accessibility. The server-to-server traffic flow is already a priority over the server-to-client requests.

Big Data implementation requires IT organizations to prepare a network deployment scale-out. Consider it as an opportunity to revisit your current networking strategy. In case, your staff is not skilled enough, consider availing consultancy and training.

Conclusion

Big Data offers a significant business analytics opportunity for proactive decision-making. Having real-time information is a competitive business advantage. It allows businesses to adjust their direction in a fast-changing market.

Big Data solutions need to accommodate the massive storage requirements and processing requirements. Analytics involves intensive processing of large volume of data. For efficiency, a company needs to invest in cost-effective infrastructure.

Big data analytics address a large volume of unrelated data from various sources. The complexity of data is not for single server or database processing. It entails computing in distributed systems dispersed in several servers. These servers are processing portions of data in parallel.

IT organization need to consider Big Data environment within the existing data centers. Big Data domain must coexist with the existing enterprise infrastructure. Achieving this means upgrading the storage scalability, parallel processing, and network accessibility.

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