Where Does the Data Come From?

If you are unfamiliar with the term, you may wonder, "what is data ingestion?". Data gets collected from various data sources, like databases and other devices. For example, the data can come from social networks, cars, IoT sensors, or app stores. It then gets transferred into a data warehouse. The data gets ingested for further analysis and usage.

Why Is Data Ingestion Necessary?

Data ingestion can save time and money. Instead of having engineers collect data, they can put their time into developing and other more complex tasks. It makes everyone spend their time more efficiently and saves the company time and money since they do not have to spend hours collecting data. Data ingestion automates tasks that engineers would have done in the past.

Ingestion can also make the data more uniform and easier to read. It makes performing statistics faster and less challenging. The data is also ready to be manipulated by engineers and data scientists. Data ingestion makes data usage easier and faster for those processing the information. Depending on the type of data ingestion chosen, companies can make better decisions based on the data they receive. It can lead to increased revenue and longevity.

Data Ingestion Types

Three types of data ingestion are options for companies. These include real-time, batch-based, and lambda architecture-based data ingestion. Real-time ingestion involves using solutions that collect and transfer data in real-time. Real-time ingestion is essential for fast-moving industries like the stock market or power grid monitoring. Organizations in these niches need accurate information constantly to make correct decisions. Real-time ingestion is the best solution if a company needs to respond rapidly to information.

Batch-based ingestion is the opposite of real-time ingestion. With batch-based data ingestion, data is collected and transferred in batches at different points in time. Companies can create a schedule that brings in the data at a particular time each day. A company may not need real-time data if it is not in a fast-paced niche.

Lambda architecture-based ingestion is a combination of both. The setup has several layers, including batch, serving, and speed layers. The first layers index the data in different groups. The speed layer takes over when the other two layers do not get any extra data present. It instantaneously indexes this data. The layers all work together to create an efficient and effective system. A company can benefit from this setup if the information is needed quickly and without lags.

Challenges of Data Ingestion

While data has changed our world significantly, especially for businesses, some challenges come with data ingestion. Companies should comply with legal requirements, such as GDPR, HIPAA, and everything else. It is up to data teams to be aware of the data privacy laws and ensure they are complying. Also, cybersecurity issues make data ingestion difficult. Teams have to deal with viruses and malicious attacks that intend to steal private data.

Data ingestion is a crucial part of Big Data architecture. Without it, data scientists and engineers would have to ingest the data themselves, which cost time and money. The different types of data ingestion can help companies make better decisions and increase the organization's chances of business success in the long term.

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