Data is the fundamental building block of any organization. Any transactional data helps analyze how well your organization is performing as well as optimize your operations for better results. With digitization making its mark everywhere, it is not an easy task to keep track of the data pouring in from all directions. Organizations have to deal with transactional logs, social data, structured, unstructured, and semi-structured data, which are all going to be captured in a digital format. The traditional methods of simply storing data in a common database go for a toss with such large volumes of data.
This brings us to why Big Data is such a massive buzzword that’s doing the rounds in the industry. Be it effective analysis and organisation of all your data, or easy accessibility and safety, big data technologies have got you covered.
Why has Big Data become so popular?
Making use of Big Data technologies to analyze the data coming into your organization helps to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences, and, ultimately, increase profitability. Capitalizing on the advantages of big data allow companies to have a competitive edge against their peers.
- Improved customer experience, engagement, and retention due to personalized offers and one-on-one contact
- Data-driven marketing can come to the forefront with Big Data
- Predictive analytics help in making informed business decisions
- Superior data security is achieved with the help of Big Data technologies
- Analyzing Big Data can give you trend-data that could help you come up with a completely new revenue stream.
Learn more - download Big Data Architecture and Performance Challenges
Key Factors in Big Data Testing
Architecture of Big Data
Simply following Big Data practices to capitalize on their many advantages is easier said than done. Improperly designed systems can lead to poor performance and any Big Data Hadoop-based architecture should satisfy the core MVP principles in accordance with the core architecture principles and guidelines.
Some of the important core components of Big Data include:
- Apache Spark-a data processing framework
- Apache Hive-data warehouse software
- Impala-a massively parallel processing SQL query engine
- Apache Kafka-a message broker project for handling real-time data feeds
- Apache Oozie-a server-based workflow scheduling system that manages Hadoop jobs
Clusters and nodes in Big Data have different hardware configurations to follow. The three different categories are master nodes which run critical management services, worker/slave nodes which run worker services as well as store the actual data, and lastly, gateway/edge nodes which run Hadoop client services.
Security Landscape
The multiple stages in security testing of Big Data applications involve authentication where unauthorized users are filtered out, authorization where who/what has access to resources is decided, data protection where only authorized users are allowed to view/use/contribute to data sets and lastly, audit where complete and immutable record of all activity is captured.
The security testing takes place at three levels in the architecture, namely, cluster level, user-level, and application level.
Performance Testing
Carrying out meticulous testing of copious amounts of structured and unstructured data is no mean feat. A proper testing approach needs to be followed. The performance testing approach for Big Data consists of five key steps:
- Setting up of Big Data cluster that requires testing
- Identifying and creating corresponding workloads
- Custom scripts/individual clients are prepared
- Execute and analyze the test cases results
- Achieve optimum configuration
Some parameters to keep in mind while doing performance testing are data storage in different nodes, variable size of the commit log, concurrency of the threads, row cache and key cache settings, connection timeout settings, and message queues.
Challenges Faced with Big Data Testing
Along with data processing and cost issues, designing Big Data architecture according to your particular requirements is a very tall order. Some other challenges frequently faced by organizations are:
- Automating, deploying, and managing Big Data technologies require someone with skilled expertise in this area. Also, automated tools may not be capable of handling unexpected issues that arise during testing.
- Virtual machine latency hinders the timing of real-time big data testing. Images are not easy to take care of Big Data.
- Generation and collection of copious amounts of data is a tedious process. The verification alone will take up a lot of time.
Big Data can make or break your business…
Every coin has two sides and so is the case with technology. Along with all the varied advantages come a set of challenges that need to be taken care of. Big Data has the potential to take your business to the next level, provided challenges like tailoring Big Data architecture for your organization, managing these systems with a specific skill set, as well as maintaining data quality and governance, are dealt with. Gear up for a faster and safer data management journey with Big Data!
About the author
Practice Director, Data, Analytics and DevOps
Vandapalli, or “Sri,” as friends call him, helps clients harness the power of data with the latest and greatest analytic technologies. With a background in IT consulting and career guidance, Sri knows how to bring clients from where they are, to where they need to be. Always developing new skills and knowledge sets, Sri hopes to leave a legacy by teaching others to achieve their goals. An author of literature on Big Data and DevOps, and a yoga and meditation instructor, Sri finds joy in public speaking and mentoring peers in his community.