First of all, let’s look at the definition of the data pipeline. The data pipeline is a sequence of data processing steps each going one by one. Every step consumes data from the previous one and feeds the result as an input to the following step. Now let’s look at why businesses may benefit from creating automated data pipelines, the common steps pipelines may have, and the tools Broscorp is using to build an effective custom data pipeline.
Why may businesses benefit from automated data pipelines?
These days, business productivity depends on speed and quality of decision-making.
Every decision should be data proved, and this can only be achieved using data from different sources and of different origins. Just a few examples:
Combining data from accounting and sales departments can help you decide about the efficiency of your business.
Collecting real-time data from the blockchain, transforming and analysing it on the fly may help create a service for investment profitability prediction.
Collecting real-time metrics from hundreds of hardware appliances can dramatically improve your maintenance practices and prevent service outages.
These are only a few of examples when your business may benefit from creating an automated custom data pipeline. So generally speaking, the overall common approach is the following:
Collect data from different sources (either internal or external).
The main advantage of a data pipeline is that it is an automated process. It runs on schedule, it runs on demand but it runs automatically without human interaction, preventing errors, drastically increasing the speed of processing, and letting you concentrate on business goals.
Common steps the effective data pipeline should have
Every ETL pipeline consists of:
Reading data from input sources.
Storing the result.
Let’s look deeper at each of the steps:
Reading data from input sources includes integration with different systems which act as providers of data. These systems expose their data in different formats (JSON, XML, Avro, Parquet) and by using different technologies (Rest API, JsonRPC, gRPC).
Transforming data means:
Cleaning data from duplicates or garbage entries.
Storing isn’t just throwing data into the database. Depending on the load, type of usage, and business needs, it can be data lake, data warehouse, or a simple relational database. When it comes to the Big data pipeline, it is especially important to store data fast enough to deliver data to the end-user as fast as possible without any losses.
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Broscorp’s approach to build effective custom data pipelines is the following
Broscorp has built a decent amount of data processing pipelines for multiple clients. And we’ve learned a lot about how to build such pipelines efficiently. Broscorp is very focused on building a pipeline which literally increases profit or decreases losses. So first of all, we collect the requirements and understand the business needs. It’s all about business needs!
Then we collect the functional requirements like formulas or analyses which should be made with incoming data. There are also non-functional requirements like the speed of processing, reliability, maintainability, etc. ’Cause literally, building a big data pipeline crunching terabytes of data is not the same as building simple data processing automation.
Based on functional and non-functional requirements, we choose from a variety of tools which we are going to use to solve your business problem.
Our toolset consists of:
Apache Flink, Apache Spark, Kafka Streams—to process data.
Apache Airflow, Apache Dagster—to orchestrate the data pipeline.
AWS RedShift, Timescale, ClickHouse, AWS S3—to efficiently store and retrieve the data.
Among languages, we are most experienced in Java and Python.
Depending on business needs, we choose the right cloud platform such as AWS, GCP, or Azure.