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Today, we are talking about a Data Pipeline. Keep reading to know how it helps companies to avoid data processing mistakes. On our channel, we share thoughts on recent developments in the tech industry, follow us not to miss new.
What is meant by a Data Pipeline?
It is a series of tools and actions for organizing and transferring the data to different storage and analysis systems. It automates the ETL process, extraction, transformation, load.
As a data pipeline example, you can collect information about your customers’ devices, location, session duration and track their purchases and interaction with your brand’s customer service.
How does a data pipeline work?
The raw unstructured data is located at the beginning of the pipeline, then it passes a series of steps and each of them transforms the data. Read further to review these steps in more detail.
#1 Collecting the data
At this stage, the system gathers the data from thousands of sources such as databases, APIs, cloud sources and social media.
#2 Extraction
After the raw data is collected, the system starts reading each piece of data using the data sources API. After the data is extracted, it goes through processing. If the sets of records are extracted and counted as one group, batch processing is applied. Real-time processing passes individual records as soon as they are created or recognized.
By default, the companies use batch processing since it is easier and cheaper.
#3 Transformation and Standardization
Now, you need to adjust the structure or format of the data. Among the most common types of transformation are
- Basic transformations in which only the appearance and format of the data is affected, without severe content changes
- Advanced transformations in which the content and the relationship between data sets are changed.
#4 Destination
This is the final point where the clean data is transferred. Further, they can go to data warehouses while less structured data is stored in data lakes.
#5 Monitoring
To ensure that the data is accurate, the engineers continuously check the pipeline data by monitoring, logging and alerting the code.
What is the AWS Data pipeline?
AWS data pipeline is a web service allowing data processing and moving it between different computing services, AWS storage and local data sources. It helps to easily create complex data processing pipeline operations, guarantee their fault tolerance and high availability.
Data pipeline is widely used for Machine learning
Tools for building ML pipelines
- We use Google ML Kit to deploy the models in the mobile application via API.
- Amazon Sagemaker an MLaaS platform for conducting the full cycle of preparing, training and deploying a model.
- Tensorflow – an open source machine learning framework developed by Google with robust integration with Keras API.
Tools for general operations with Data Pipelines
- For ETL, Data preparation and Data integration tools: AWS Glue, Informatica PowerCenter, Apache Spark, Talend Open Studio.
- Data warehouse tools: Amazon Redshift, Snowflake, Oracle.
- Data lakes tools are offered by such providers as Microsoft Azure, IBM and AWS.
- Batch schedulers: Airflow, Luigi, Oozie or Azkaban.
- Stream processing tools: Apache Spark, Flink, Storm, Kafka and Amazon Kinesis.
This blog was prepared by the EZtek team. EZtek helps top brands worldwide to innovate and accelerate digital transformation. We provide world-class enterprise software engineering, design and technology consulting services.
