Data Engineering helps you in collecting and validating your data
Data engineering focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information.
Data Engineers are there where the rubber meets the road, focusing on the applications and collecting of Big Data. Through data engineering, mechanisms are put in place to apply Big Data into real-world operations.
How can data engineering help you?
Set up a distributed processing framework with Apache Spark enabling you to split processes and run them in parallel
Build a continuous processing framework with Apache Kafka for the processing of real-time data as it is produced and received.
Execute processing jobs by orchestrating them between nodes and executors to complete complicated analytical workloads
Scheduling & orchestration
Schedule jobs in your data pipeline and manage dependencies and errors with Apache NiFi as a wider dataflow solution
Build a data governance program to focus on data definitions and standards, quality, security, privacy, architecture and integration
cloud data platforms
Leverage the flexibility of AWS and Azure Cloud to build fully interoperable, cost-effective and expansive data platforms
hybrid cloud solutions
Keep your on-premise data infrastructure and mix it with the computing and storage services of AWS and Azure Cloud
Build ETL pipelines to extract data from an input source, transform the data and load it into an output destination
Build automated tests in your data pipelines to validate data flows and catch costly production errors
This is just a grasp out of many possible fields where Data Engineering can offer tremendous value.
Curious what possibilities lie in your organization?
Over the past few years, many new technologies have made their advances in the data processing world. At Infofarm, we use these technologies to build highly performant and scalable data platforms for our partners.
Data platform components
Building a future-proof and sustainable data platform is a complex task, involving a bunch of important components we need to carefully take into account.
Get your data into the platform from various sources
Store data in a future-proof and efficient manner
Generate a clean and uniform data format
Think of a good way of accessing your data from various applications
Process and combine your data for different use cases
Monitor the operations and performance of your platform
Ensure the stability and durability of your data
Adhere to regulatory privacy requirements
Make sure everybody knows where the data is coming from and what it means
Ensure your data is being stored in a secure way
Orchestrate all your data processes in an automated way
Archive the data that is less frequently used
Visualize and explore data in your platform
How can we help you?
Do you want to start leveraging value from your data by using Data Engineering techniques? We can support you in every step of the data-driven value chain.
Brainstorm sessions to come up with valuable use cases and a step-by-step implementation roadmap.