Data is the new oil, but it yields no value when it merely resides in a system. It takes data engineering to prepare and supply it to data scientists, who plumb its depths and extract value. From creating data pipelines to processing data, storing, and enabling access to the processed data, our data engineering services run the whole gamut of processes that make data ripe for analysis.
Various Stages in Data Engineering
Data Processing (Batch or stream)
Processing data to get the desired results.
Physically or logically breaking up data across servers.
Gathering all the required data.
Storing data for efficient processing and retrieval.
The crucial first step in data engineering is to move the data from its multiple sources into a system where it can, depending on the requirement, be stored or processed immediately. With sensors, beacons, machine logs, GPS, clickstream, APIs, or even databases, data sources widely differ and so do the formats, volumes, and protocols. Data from diverse sources needs to be ingested, re-formatted, and combined for meaningful analysis. Our data engineering support ensures that this fully automated process runs without a hiccup.
Processing big data without a clear road map and well-defined use case is not just futile but highly expensive. The newly emerging tools for big data processing require judicious selection, considering the various trade-offs involved. A significant question that arises in the context of big data processing is whether you should go for stream or batch processing. The answer depends on the nature of data, the desired response time, and the nature of your application.
More on Big Data Processing
Data stores are not all of the same stripe. Compared to relational database management system (RDBMS), which guarantees atomicity and consistency, non-relational databases offer the much-needed flexibility and scalability in operations. NoSQL databases allow the data model to evolve with the application making them ideal for fast and iterative development. Since these new-generation databases are optimal for storing and processing certain types of data, their selection requires careful judgment.
More on Choosing the Right Datastore
Big data is never stored in a single block. Rather, it is broken into self-contained blocks that are easy to maintain and access. The chunks can be stored in different types of databases, tapping into their unique features. Data partitioning improves application performance since operations are run on smaller chunks of data. Splitting data across many nodes also protects applications from a single point of failure. By partitioning data appropriately, our data engineers ensure your application’s efficiency, scalability, and availability.