Cloud Data Ingestion
Our capabilities help to explore possible sources of data and build a robust Data Ingestion mechanism to collect, ingest, and manage multiple data sources.
What WE Do?
Data ingestion is a crucial success factor for analytics and business intelligence. The more rapidly an organization can ingest data into an analytics environment from disparate production systems, the stronger analytics insights can be.
Too frequently though, data ingestion methods evolve into a complex collection of narrow-purpose scripts and tools which, taken as a whole are difficult to maintain and even harder to update.
PlatingNum provides a better way, with a unified data ingestion solution that’s versatile and easy-to-use. We design data ingestion solutions to eliminate headaches from data ingestion by helping to automate and optimize the method.
We are adept at coping with data lakes, ingestion pipelines, cloud platforms, and data integration for structured and unstructured data. Our methodology is focused on ensuring that our data engineering techniques reduce your operational costs, explore new information and revenue sources, and accelerate new product development.
- A data ingestion pipeline pulls streaming data and batched data from pre-existing databases and data warehouses to a data lake.
- Data pipeline architecture is the design and structure of code and systems that copy, cleanse, or transform as needed, and route source data to destination systems such as data warehouses and data lakes.
- The data ingestion layer processes incoming data, prioritizing sources, validating data, and routing it to the best location to be stored and be ready for immediately access.
- Apache Flume, Apache Nifi and Elastic Logstash are data ingestion tools
Real-time data ingestion for analytical or transactional processing allows companies to make timely operational decisions that are critical to the success of the organization – while the data is still current. PlatingNum supports real-time data ingestion from sources including databases, log files, sensors, and message queues and delivery to targets that include Big Data, Cloud, Transactional Databases, Files, and Messaging Systems.