Building Data Pipeline Architecture- the Journey from Ingestion to Analytics

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Businesses today are fueled by the enormous volume of data. It is their most valuable asset that provides them with valuable insight and drives profitability. However, businesses need to collect these data from multiple sources, and this is where data ingestion comes into play.

What is data ingestion?

Before getting into the formal definition of data ingestion, let’s take a look at an example of a luxury clothing brand that has announced the launch of a new product through popular social media platforms, Twitter and Instagram. The announcement created a buzz, and fashion enthusiasts from all over the world interacted with the brand and shared their thoughts, thus resulting in a large volume of data.

The brand wishes to analyze the data from all these platforms to get an insight into the impact of the announcement across the global market. Data ingestion will help the brand to collect data from all the sources into a data warehouse and use it for customer sentiment analysis.

Therefore, simply put, data ingestion is the process of moving data from any source to a landing, such as a data warehouse or data lake. It is a step in the data pipeline architecture that lies between data generation and data analysis. Data analysis is not possible without data ingestion.

Data ingestion gives the organization an insight into its performance and several other components across multiple business verticals, aiding them in improving and streamlining processes.

Building the data pipeline:

Data pipelines are the pathway through which raw data transits from data sources (SaaS platforms and other platforms) to data warehouses, where the raw data is analyzed using Business Intelligence tools.

Factors impacting speed in data pipeline:

The speed by which data moves through the pipeline is of special significance, and three factors can impact this speed. They are:

1. Latency:

It refers to the time required by a single unit of data to traverse through the pipeline.

2. Throughput or rate:

This indicates the volume of data that can travel through the pipeline in a given amount of time.

3. Data pipeline reliability:

It is important that all the devices within the pipeline are fault tolerant. Furthermore, to ensure data accuracy, the pipeline should have validation and logging mechanisms.

The journey from ingestion to analytics:

The data pipeline can be divided into 5 parts, which are:

1. Source:

It is the first layer in the data pipeline architecture. The source is the point of origin for all organizational data. SaaS platforms such as Salesforce or SAP or even social media platforms can act as the source.

2. Ingestion:

Each data source offers an API or application processing interface that facilitates the ingestion process. Here the data is read and extracted from the source using different approaches. In the next section, these methods are discussed in brief. During data ingestion, data profiling is done based on its feature, relevance to business objectives, and structure.

3. Modification:

This step is self-explanatory, where the gathered data undergoes some transformation in its structure, filtration, and aggregation.

4. Destination:

The last step in this pipeline is the destination of the data. The initial destination is the data warehouse that stores the cleaned and processed data for further use by the organization. From here, the data traverses to analytics tools like Tableau.

5. Monitoring:

This can be considered as the bonus step in the data pipeline, aimed at determining the well-being of the system. Data pipelines are complex systems consisting of software, hardware, and networking components. Each of these components is subject to failure. Therefore, developers need to build a code with monitoring and alerting facilities, enabling data engineers to keep the pipeline operational, manage performance and resolve crises.

These five steps summarise the data pipeline architecture and show how data moves from the source and gets analyzed via data ingestion. But how is data ingestion done? Here is the answer.

Methods of data ingestion:

Now that you have to know how to build a data pipeline successfully, here are three primary approaches to data ingestion:

1. Batch data processing:

This technique helps process a large quantity of data in batches after specific intervals. For instance, a company ingests social media data every Friday afternoon.

2. Real-time data processing:

Real-time data processing is done during the rum time for small chunks of data. Financial companies heavily rely on real-time processing.

3. Streaming data processing:

It is a data ingesting method where data is constantly processed. Streaming data processing is widely used in the stock market.

The data ingestion approach varies from company to company, and it is essential to carefully evaluate all the scopes and use cases before selecting the best-suited method. However, data ingestion is a complex process riddled with challenges. Some of the common challenges are discussed below.

Challenges of data ingestion:

Data ingestion is an essential part of the data pipeline. However, there are specific challenges involved in data ingestion:

1. Laws and regulations:

Data used by organizations is monitored by several rules and regulations across the globe. While these regulations protect consumers’ interests, organizations often face the heat. They are bound to adhere to these laws and regulations, such as General Data Protection Regulation has to be followed by European countries to stay ethical.

2. Data Quality:

During data ingestion, maintaining the completeness and accuracy of data is very important since it is crucial for business intelligence transactions. Nevertheless, it is one of the prime challenges faced during data ingestion.

3. Syncing data:

As the company proliferates, so does the amount of data generated from multiple sources. Ingesting such a massive pile of data from different sources into a single warehouse is challenging.

3. Process issue:

While processing a large volume of data, speed is a significant factor. However, in case it is missing, efficiently maintaining data pipelines becomes an arduous task.

If you are looking to create a fool-proof data pipeline that eases the journey from ingestion to analysis and can capture data from production systems without hampering database performance, then Aseco is your destination.

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