In the times when the volume and speed of data are increasing at a fast pace with the growth of the Internet of Things, the need for an effective analytics system has risen substantially.
Apart from volume, data is now available in a wide variety of formats. Storing and using such varied and heavy data is a mammoth task especially during the absence of technology. Enterprises need to formulate an efficient data ingestion strategy for handling such large volumes of data. In this blog post, you’ll learn about data ingestion along with its benefits. Also, find how data ingestion deals with Big data.
Data Ingestion
The process of obtaining and importing large streams of data in structured, semi-structured, and unstructured or heterogeneous formats either for immediate use, prepare data for analysis, or database storage is called data ingestion.
Data ingestion, in its broadest sense, involves a focused dataflow between source and target systems that result in a smoother, independent operation.
Data ingestion either occurs in real-time or in batches i.e., either directly when the source generates it or when data comes in chunks or set periods. However, whether real-time or batch, data ingestion entails 3 common steps. Let us find what these steps are:
Step 1: Data Extraction – This step involves retrieving data from various sources. Organizations can use the extracted data for either further analysis or storage in a data repository.
Step 2: Data Transformation – Data transformation involves the conversion of data from one format to another. This step includes a number of processes such as validating, cleaning, and normalizing data to guarantee its sanctity and reliability.
Step 3: Data Loading – The Data loading step involves routing or loading data in a storage system such as a cloud data warehouse for analysis.
With the increase in data volume, variety, etc., these steps of data ingestion will increase without the shadow of a doubt.
Not quite so long ago, data ingestion processes were executed with the help of manual methods. In doing so, organizations used steps like manual data gathering and manual importing into a custom-built spreadsheet or database. However, due to inaccuracies and the rise of errors, manual methods have been replaced with automated ones. It gives rise to trustworthy data that can be used to deliver accurate, actionable insights, thereby helping companies make better business decisions.
Enterprises today make use of software-centric platforms to streamline the data ingestion process. The automated data ingestion approach offers numerous advantages, but before determining what these advantages are, let us find why it is necessary.
Need for Automated Data Ingestion
Enterprises use modern-day technologies to carry out heavy data-intensive operations. With the increase in data, insights that were earlier collected from 5000 people are now collected from 50, 000 people (bigger the scale, better are the results).
Enterprises relying on conventional data ingestion software have to face lengthy battles in processing data for further usage. Downtimes become extremely common as the underlying system fails to process large streams of data with accuracy. In addition, manual ingestion methods are cumbersome and tedious, and so operations slow down.
Why Automated Data Ingestion?
The right way to counteract these pitfalls is to adopt an automated data ingestion approach. It allows teams to process data accurately without the need to rely on heavy coding and additional infrastructure. Hence, even non-technical business users can reap benefits from these tools.
With the help of automated data ingestion tools, teams can process a huge amount of data efficiently and bring that data into a data warehouse for analysis. The data can be cleansed from errors and processed proactively with automated data ingestion software. A lot of data can be processed without delay. Plus, a huge sum of money and resources can be saved. As a result, silos can be eliminated and data lakes can be built, ensuring continuous success from their digital initiatives.
Benefits of Automated Data Ingestion
The benefits of automated data ingestion are immense. Here are some of them.
1. Improved Time-to-market: 55% of B2B enterprises have to face with the inability to combine data from a variety of sources promptly that holds them back from accomplishing end goals.
Automated data ingestion can help companies prepare for data within a particular time frame, accelerating their time-to-market goals. With improved time-to-market, enterprises can gain a competitive edge and grab a larger market share.
2. Increased Scalability: For many automatic data ingestion methods may sound overwhelming. The truth is, these platforms are easy to work with and can scale. Meaning, initially, companies can pick one or two data sources and extract data through automation. As they grow, they can scale up automating more data over time.
With the use of self-service data ingestion tools, this can become easier. These tools help companies save a lot of valuable time, thus enabling them to generate faster time-to-value.
3. Reduced risk: Automated data ingestion methods can improve the quality of data being processed. They mitigate other significant risks such as the risk of human error in extracting, transforming and loading data. The extracted insights can help enterprises make informed business decisions. As a result, customers can savor a delightful experience.
4. Enhanced IT Productivity: In the absence of right technological platforms, IT teams have to deal with difficult operations such as extracting data from various apps, transforming formats with custom code, and loading data into various siloed systems. They are not able to focus much on the governance role, and so their productivity decreases. With the introduction of automated data ingestion methods, IT teams are freed to take part in the operations and focus on governance instead. Moreover, a lot of focus can be drifted to the wider challenge which is to meet the changing consumer’s expectations with minimum overhead.
Conclusion
In short, automating data ingestion process offers a lot of benefits. It is scalable and helps companies use data without risking their time-to-market goals. It also increases efficiency and saves time and money as well. In addition, automation streamlines a lot of tasks, thus allowing teams to focus on governance activities.
Need any other reason to employ an automated data ingestion software?
