Big Data Modelling
Data Model is like 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 need to be done. 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 may 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 modelling of Big Data. We have modeled significant number of Big Data systems considering all the relevant inputs to proficiently manage the data. With the vast experience in Big Data Modelling, we will guide and support your organization to model the Big Data Ecosystems.
Big Data Development
Our experienced big data consultants and engineers will help to implement end to end implementation of the Big Data Projects. We take care of Data Acquisition and Ingestion, Big Data Storage, Data Processing and Data Visualization. Be sure that you will get our professional advice about whether to deploy the solution on premises or in the cloud. We will 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. Our team sets up cluster management depending on the load to ensure great working efficiency and optimized costs.
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 prioritizing data sources, validating individual files and routing data items to the correct destination. We choose the most appropriate tool specific to a project from different open source data ingestion tools or tools available on cloud .
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 storage of unlimited amount of data, cope both with high rates of random write and read access, flexibly and efficiently deal with a range of different data models, support both structured and unstructured data. There are challenges like Volume, Velocity, Variety in storing the Big data. We address these challenges by making use of distributed, shared nothing architectures. This allows addressing increased storage requirements by scaling out to new nodes providing computational power and storage.
Big data processing encompasses set of techniques used prior to the application of a data mining method as large amount data will likely 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, cleaning 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 will be reliable and suitable for any further processing or downstream applications.
We undertake projects of 1. Batch Processing 2. Stream Processing 3. Real time processing.
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 visualization tools with the processing tools to depict the insights or patterns through suitable tools.
Big Data Testing
The ultimate use of big data is its ability to give us actionable insights. Poor quality data leads to poor analysis and hence to poor decisions. Errors in data of pharmaceutical companies or banks regulations may lead to legal complications.Ensuring accuracy of data will lead to correct human engagement and interaction with the data system.
We ensure the quality of the data by 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-gritties of the Big data development and masters of testing methodologies.
Validation of data in storage systems–HDFS or any Cloud Storage. It includes the comparison of source data with the added data.
Process validation involves Business Logic validation, Data Aggregation and Segregation, checks key-value pair generation in different parallel processing models like Apache Spark.
Involves the elimination of data corruption, successful data loading, maintenance of data integrity, 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 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 this manually to start with and eventually automate based on the complexities of the project.
Data processing is the core of the Big Data implementation. In this type of testing, the primary focus is on all the types of Big data processing tasks and Big Data Operations. Whenever the ingested data processes, validate whether the business logic is implemented correctly or not. And further, validate it by comparing the output files with input files.
The output stored in HDFS or Azure Data Lake or Google Cloud or AWS S3 or any other warehouse. The tester verifies the output data correctly loaded into the warehouse by comparing the output data with the warehouse data.
Big Data visualization testing involves 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.