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MongoDB Atlas offers automatic sharding, horizontal scalability, and flexible indexing for high-volume dataingestion. Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more.
Thus, making it easier for analysts and data scientists to leverage their SQL skills for BigData analysis. It applies the data structure during querying rather than dataingestion. How Data Flows in Hive In Hive, data flows through several steps to enable querying and analysis.
A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. DataIngestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.
Data flow Here is an example of this data flow for an Agent Creator pipeline that involves dataingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. He currently is working on Generative AI for data integration. The next paragraphs illustrate just a few.
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