Big Data and Data Engineering Services:
We have built capabilities around Big Data platform implementation from ETL, data processing, compute, data orchestration, visualization, reporting, analytics, advanced and predictive analytics, data modelling and data science. Leveraging these capabilities we offer end to end Big Data and Data Engineering services.
End to End Data Lake Implementation
We help businesses design, architect and implement data lake frameworks and integrate data assets to derive meaningful insights without any data loss. The implementation consists of identifying data channels, data integration, backup, archive, data processing, data orchestration, and visualization along with data governance and automation.
Big Data DevOps & Managed Services
Leveraging our expertise in both DevOps and Big Data Administration, we ensure architecture setup, implementation with full automation and manage the overall performance of Hadoop clusters to ensure high throughput and availability. We also help businesses identify potential threats through, data governance and access & identity management to help ensure data security.
Big Data Testing & Automation
We ensure data quality, accuracy, consistency and completeness through big data testing and automation. Our QA engineerings verify data in a 3 stage validation including data stage validation, MapReduce Validation and output validation followed with performance testing of big data applications.
Data Strategy, Consulting & POC
We help businesses to determine their big data strategy and consult on improving the business performance uncovering the power of data. Our Big Data consulting includes POC/POV, technical recommendations, data source analysis, architectural consulting, capacity planning and much more.
We can help businesses with real-time data ingestion, ETL & batch processing and storage from different & complex data sources leveraging our deep expertise across big data technologies such as Hadoop (HDFS, Map Reduce, Hive, Flume, Sqoop, and Oozie) and Spark. We help businesses create real-time charts & dashboards and setup pipeline.
We use various tools such as Tableau, Chart.js, Dygraphs, D3JS and HighCharts to produce visuals and stories that generate high business impact. We generate custom dashboards, reports, alerts and metrics as per business logic and apply machine learning algorithms & data modeling to perform predictive analysis using techniques such as regression and decision trees.
Let’s have a look at the baseline skills for a data engineer. Of late, data engineer roles have gained more importance in organisations that are facing a data deluge, with data lying around in multiple formats in organisations. The role of data engineer needs strong data warehouse skills with a thorough knowledge of data extraction, transformation, loading (ETL) processes and Data Pipeline construction. Big Data engineering is a specialisation wherein professionals work with Big Data and it requires developing, maintaining, testing, and evaluating big data solutions. Big Data engineers are trained to understand real-time data processing, offline data processing methods, and implementation of large-scale machine learning.
Since Big Data engineering is a demanding specialisation, having sufficient experience with software engineering is a prerequisite to enter the field. In addition to this, a familiarity with coding and testing patterns, object-oriented designs, as well as experience working on open source software platforms would give students an additional benefit. It would be even better for them to have expertise in NoSQL and data warehousing as well.
Big Data engineers are tasked with building massive big data reservoirs and highly scalable and fault-tolerant distributed systems, that can inherently store and process massive volumes or rapidly changing data streams. They are also responsible for developing, constructing, testing, and maintaining frameworks like large-scale data processing systems and databases. Once data flow is achieved from these pools of filtered information, data engineers can then incorporate the required data from their analysis.
3 months. This is a very rough estimate, actual time may vary.
We have built capabilities around Big Data platform implementation from ETL, data processing, compute, data orchestration, visualization, reporting, analytics, ML and AI.