Data Ingestion is the process of collecting raw data from one or more sources into a target location for processing and analysis. It helps organizations extract key insights of ever-expanding data in real-time. The main challenge with data ingestion is that it can compromise data security and compliance regulations making it complex and costly. Businesses currently use the three types of data ingestion – Batch-based Data Ingestion, Real-time Data Ingestion, and Lambda Architecture-based Data Ingestion depending on their specific data needs. The constant flow of unstructured data is expected to increase the demand and growth of data ingestion tools in the market. The COVID-19 pandemic has led to an increase in availability of unstructured data leading to the anticipated expansion of cloud-based data ingestion technologies.
02
Jun
Tags:
apacheflume, apachekafka, apachenifi, bigdata, cloud, compliance, COVID19, daas, data, Data Domain, dataasset, database, datacollection, datadefinition, dataengine, dataingestion, dataloader, datamanagement, datawarehouse, etl, Google, knowyourdata, KYD, lambdaarchitecture, risk, RZOLUT, technology, unstructureddata, visualblog