What Are Data Ingestion Tools? (With Best Practices)

Updated 7 September 2023

Data professionals employ data ingestion pipelines to manage the scope and scale of business data demands. Having many data pipelines running continuously throughout the entire organisation without the direct intervention of the development team allows for unparalleled scale in achieving important business goals. Data ingestion has grown into a critical component of self-service platforms for analysts and data scientists seeking data for real-time insights, deep learning and AI workflows. In this article, we define what data ingestion tools are, review ways of ingesting data, discuss the benefits of ingesting data for businesses and review best practices for data ingestion.

What are data ingestion tools?

Data ingestion tools are software platforms that extract data from a variety of information sources and facilitate the movement of these data streams to a central warehouse. Data Ingestion is the process of ingesting massive amounts of information from various external sources to perform analytics, data mining and other business functions. The tool simplifies the process by providing a framework that makes it simpler to harvest data from various sources and sustain a variety of data transport protocols. In recent years, more organisations have understood the potential of data as the most pragmatic means to gain actionable insights.

Manually extracting company data from different sources can be time-consuming and expensive. These tools eliminate the need for coding individual pipelines for each data source while speeding up data processing by delivering data to integration software or directly into a data warehouse. A tool for data ingestion aids in the sorting, modification and formatting of data collected, helping businesses to undertake data analysis procedures more effectively.

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What are the different ways of ingesting data?

A user can perform data ingestion in three ways, namely in real-time, in batches or in a hybrid of the two in a configuration known as lambda architecture. Here is an explanation of each method:

Real-time data ingestion

Real-time data ingestion involves gathering and transporting data from different sources on a real-time basis using workarounds such as change data capture (CDC). CDC actively monitors transaction and redo logs and transmits altered data without interfering with database workload. Real-time ingestion is critical for time-sensitive case scenarios such as stock market trading, reading medical information such as heart rate and blood pressure or power grid monitoring, where organisations require making quick operational decisions or reacting rapidly to emerging insights.

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Batch-based data ingestion

Batch-based data ingestion entails collecting data and transferring the data in batches at regular intervals. The ingestion layer can collect data based on simple schedules, trigger events or any other predetermined ordering. This technique is useful when an organisation requires collecting particular data points daily or simply does not require data for real-time decision-making.

Lambda architecture-based data ingestion

The Lambda architecture is a data ingestion configuration that incorporates both real-time and batch techniques. Lambda architecture includes batch, serving and speed layers. The batch and serving layers index data in batches, while the speed layer indexes data in real-time. This is the data that is yet to be handled by the batch and serving layers, which is slower. This continuous exchange between layers virtually guarantees that data is available for querying with minimal latency.

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Benefits of a data ingestion tool for businesses

Data ingestion software tools come with various features and capabilities to help businesses manage their data better. Here are some of the main benefits a company or organisation can realise by using such a tool:

  • Faster data extraction and delivery: A data ingestion software tool helps firms in extracting data from its sources and ingesting it to the desired destination in a timely manner. Saving time and energy is critical for businesses because it increases productivity and revenue.

  • Scalability: These tools are effective for dealing with a large volume of data for businesses of all sizes and remain functional even with increased data load. Growing businesses typically work with a small dataset, whereas large-scale enterprises often work with lots of data.

  • Cost-effectiveness: They are cost-effective because they eliminate the increasing and recurring costs of manual methods. Businesses can save money and reinvest it in other important activities.

  • Easy to learn for unskilled users: These tools are user-friendly with most of them having drag-and-drop functionality for adding or subtracting data sources and selecting data destinations. This is simple to learn and eliminates any need for specialised knowledge and lengthy training sessions.

  • Accelerate customer onboarding: Speedy data ingestion allows for quicker customer onboarding. Being able to handle new data in real-time is crucial to ensuring that the service is providing value to the new customers.

  • Leads generation: When a company can rapidly transform inbound data, it can make informed decisions. Data ingestion capabilities assist businesses in capturing and transforming data that is used to generate leads and sales in real-time.

  • Data accessibility: Data ingestion enables firms to collect data from multiple locations and move it to a centralised area for instant access, retrieval and analysis.

  • Better data management: Businesses can organise data more efficiently using these tools, resulting in fewer inconsistencies, redundancies and enhanced data quality.

  • Data profiling and cleansing: They offer a variety of sophisticated profiling and cleansing features to help simplify data conversion. This is beneficial to data analysts because it saves them a significant amount of time in the data cleaning process.

  • Allows faster data transformation: Since data ingestion techniques can capture data in real-time, they remove the necessity of batch processing. Professionals working with this data can improve, standardise and process it as soon as the data enters the ingestion layer.

  • Teams create better software: Software developers can use these tools to ensure their software applications transmit data faster, providing users with a better experience.

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Best practices for data ingestion

Here are some best practices for data ingestion:

Network scalability

The data pipeline requires matching the business traffic. Network capacity scalability is the most challenging aspect of the data pipeline because of the fluctuating traffic. Using data ingestion software tools is essential to throttle bandwidth and compress data.

Support for unreliable network

The data ingestion pipeline accepts data with a variety of configurations, such as visuals, audio, text documents, table format files and log files. Because of the differential speed of information transmission, it may pass through an unreliable network. Ensuring that a data pipeline can accommodate diverse data is a good practice.

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Picking the right data format

Data ingestion technologies require providing a format for data serialisation. Because data comes in different formats, converting it into a uniform format provides an easier way to analyse and understand the data. The data ingestion pipeline also requires using diverse data source technologies and operating systems.

Automating the process

When the quantity of data was small, the procession of data ingestion was manual. With an enormous amount of data being created every day, it is no longer possible to carry out the manual processes of modelling, cleaning and processing this data. It is a good practice to create techniques that digitise and expedite this process so that professionals who work with this data spend more time analysing it instead of focusing on the manual processes.

Data streaming

Professionals analyse data in batches, streams or in real-time, depending on business needs. Sometimes, it may require to have both the processing methods in the data ingestion pipeline. Having an ingestion tool that supports both is vital.

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Latency and accuracy

Data that is more recent can help companies make more agile decisions. Real-time data extraction from application program interfaces (APIs) and databases can be challenging. Many target data sources receive data in chunks rather than in streams. Having verifiable data is crucial for gaining the trust of data consumers. When modifying data in the data ingestion process flow, it is a good practice not to discard input data or transitional forms.

Data governance

After cleaning your data, it is essential to keep it that way. This entails instituting data governance, with a steward in charge of the quality of every data source. This involves outlining the data model and cleansing rules, determining which data require to be ingested into which data sources and managing the treatment of unclean data.

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