Big Data Modeling

Big Data Development

Big Data Testing

Big Data Modeling

Data Model is like an architect’s building plan, which helps to build a conceptual model and set the relationship between data items. The goal of data modeling is to formally explore the nature of data to figure out what kind of storage is needed and what kind of processing needed. Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and policies on the data.

Modeling Big Data depends on many factors, including data structure, which operations need to be performed on the data, and what constraints are placed on the models. We need to take into consideration of Data Ingestion, Data Storage, Data Operations, Data Scalability, and Security for modeling of Big Data. We have modeled the significant number of Big Data systems considering all the relevant inputs to manage the data proficiently. With the vast experience in Big Data Modeling, we guide and support your organization to model the Big Data ecosystems.

Big Data Development

Our experienced Big Data consultants and engineers support an end to end implementation of the Big Data Projects. We make collaborative efforts to bring order to your Big Data. Our team of senior-level consultants helps in implementing the technologies required to manage and understand your data, enabling you to predict customer demand and make better decisions at the right time.

We handle all the stages of Big Data development – Data Acquisition and Ingestion, Big Data Storage, Data Processing, and Data Visualization. Be sure that you get our professional advice about whether to deploy the solution on-premises or in the cloud platform. We help you calculate the required size and structure of clusters customized to the nature of the implementation. We install and tune all the required frameworks, making them work seamlessly, as well as configure the software and hardware. Analyzing your business challenges well, we offer you the strategic guidance needed to succeed, leveraging the power of data you accumulate, to your advantage.

Big Data ingestion is a process of connecting to disparate sources, extracting the data, and moving the data into Big Data stores for storage and further analysis. We are experts in prioritizing data sources, validating individual files, and routing data items to the correct destination. We choose the most appropriate tool specific to a project out of multiple options available from open source data ingestion tools or tools provided by cloud solutions providers or building a data ingestion tool.

Big Data storage is concerned with storing and managing data in a scalable way, satisfying the needs of applications that require access to the data. The ideal Big Data storage system would allow the storage of an unlimited amount of data, cope both with high rates of random write, and read access. The storage system flexibly and efficiently deals with a range of different data models, supports both structured and unstructured data. There are challenges like Volume, Velocity, Variety in storing Big data. We address these challenges by making use of distributed, shared architectures. Choosing an optimal storage system allows addressing increased storage requirements by scaling out to new nodes providing computational power and storage.

Big Data processing encompasses a set of techniques used before the application of a data mining method as large amounts of data likely to be imperfect, containing inconsistencies and redundancies and not directly applicable for a starting a data mining process. Big Data processing includes a wide range of disciplines, data preparation, data reduction techniques, data transformation, integration, cleansing, and normalization. We have a vast experience in applying the optimal techniques by choosing the appropriate tools to process the data as per the client project needs. After the application of a successful Big Data preprocessing techniques implemented by our team, the final data set obtained reliable and suitable for any further processing or downstream applications.

Big Data visualization refers to the implementation of more contemporary visualization techniques to illustrate the relationships within data. Visualization tactics include applications that can display real-time changes and more illustrative graphics, thus going beyond pie, bar, and other charts. We can seamlessly integrate the suitable visualization tools with the processing tools to depict the insights or patterns.

Big Data Testing

The ultimate use of Big Data is its ability to give us actionable insights. Inferior quality data leads to poor analysis and hence to poor decisions. Errors in data of pharmaceutical companies or banks or investment firms may lead to legal complications. Ensuring accuracy of data leads to correct human engagement and interaction with the data system.

We ensure the quality of the data by a rigorous and detailed end to end testing across the different stacks of the Big Data ecosystem. As a team of Big Data SDETs(Software Development Engineers in Testing), we understand the nitty-gritty of the Big Data development and fundamentals of testing methodologies.

Data Validation

Data Validation ensures the accuracy and quality of the data in different storage systems.

Process Validation

Process validation involves business logic validation, data aggregation, and segregation, checks key-value pair generation in different parallel processing models like Apache Spark.

Output Validation

Output validation involves the elimination of data corruption, successful data loading, maintenance of data integrity, and comparing the storage data with target data.

The primary purpose of the Data ingestion testing is to verify that the data adequately extracted from multiple sources and correctly loaded into a storage layer or not. The storage can be on the premises HDFS or Azure Data Lake or Google Cloud or AWS S3. Tester has to ensure that the data properly ingests according to the defined schema and also have to verify that there is no data corruption. The tester validates the correctness of data by sampling the source data, and after ingestion, compares both source data and ingested data with each other. We achieve the process of validation manually during initial stages but automate later depending on the complexities of the project.

Data processing is the core of Big Data implementation. The primary focus here is on all the types of Big Data processing tasks like transformation, integration, cleansing, and normalization and Big Data Operations. We design our test cases considering the processing tasks specified in the requirements. We meticulously validate duplicates, data corruption, business logic, data aggregation, segregation, checks key-value pair generation in parallel processing models like Spark. We perform the validations manually and automate the feasible validation scenarios.

Big data is stored in HDFS or Azure Data Lake or Google Cloud or AWS S3 or any other Big Data storage. The tester verifies the data from different source systems correctly loaded into the Big Data storage by comparing the source data with the data in storage. We validate the storage for the flexibility of storing the different formats – Structured, Unstructured, and semi-structured data.

Big Data visualization testing involves the validation of the format, computations, flow of the analyzed data in tools like Tableau. We thoroughly verify the format as per the requirements specifications. We simulate the computations manually and validate the requirements.