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Building Scalable Data Pipelines to Power Big Data Applications

A scalable data pipeline sending a stream of binary code to a big data application.


Modern businesses create massive amounts of valuable data every day that could be used to make smarter and more innovative business decisions. However, the average company only analyzes 37-40% of its data. Big data applications can analyze large volumes of data very quickly, providing visualizations of current business insights, recommending actionable steps to improve processes, and predicting future outcomes. Big data applications rely on data pipelines that can ingest, transform, and load a high volume of business data both quickly and efficiently. This blog provides tips for building scalable data pipelines that support big data analytics.

Building Scalable Data Pipelines

A typical data pipeline consists of four basic stages:

  1. Data discovery: Locating and classifying data based on characteristics like data structure, value, and risk. This also involves determining the quality of data and understanding the different sources.
  2. Data ingestion: Pulling data from multiple sources into a single pipeline via technology like API calls, webhooks, and replication engines.
  3. Data transformation: Altering the format and structure of data, optimizing it, and improving the quality.
  4. Data delivery: Moving data to its ultimate destination, such as a big data platform.

To make data pipelines more scalable, you should employ automation technology to find, classify, and ingest data. You also need scalable big data storage, an end-to-end system, and data monitoring to ensure peak efficiency and secure data. Here are some tips for building scalable data pipelines for big data applications.

Automatic Data Discovery and Classification

Before data goes into the pipeline, it must first be located and classified. Data classification is a necessary step for ingestion into the pipeline. Classification also enables more intelligent analysis by big data applications.

Automatic Data Ingestion

Scalable data pipelines use automation technology like API calls, webhooks, and replication engines to collect data. There are two basic approaches to data ingestion:

  • Batch ingestion takes in groups (or batches) of data in response to some trigger, such as reaching a particular size or file number limit or after a certain amount of time has elapsed. 
  • Streaming ingestion processes data in real-time, pulling it into the pipeline as soon as it’s been generated, located, and classified.

Big Data Storage

In the last stage of the pipeline, data is loaded to its final destination, where your big data application will analyze it. Historically, on-premises big data pipelines used Hadoop File System (HDFS) data warehouses as the destination. However, a more scalable solution is to use a cloud native data architecture such as Google BigQuery or Amazon AWS. Cloud platforms use elastic storage, which means you can easily scale services as your data volume grows or shrinks.

Monitoring and Governance

To ensure accurate analytics, you must ensure that the pipeline runs smoothly and the data is accounted for and processed. End-to-end data pipeline monitoring provides visibility into the pipeline's performance and the data's integrity.

Data governance is critical if you process any regulated data, such as health records or credit card payments, or if you do business in regions subject to data privacy laws like the GDPR. With end-to-end data pipeline monitoring, you can track data from ingestion to delivery, maintaining a clear chain of custody and ensuring no data falls between the cracks. It’s also important to implement security monitoring and role-based access control (RBAC) on the data analytics platform to maintain data privacy and compliance.

Building Scalable Data Pipelines with Copado Strategic Services

Scalable data pipelines use automation, elastic big data storage, and end-to-end monitoring to power big data applications. In the push to quickly and efficiently analyze data for business intelligence, it’s important to maintain the security of your pipeline and the privacy of your critical data. That means you need to integrate security into every step of the pipeline.