Continuous ETL Solutions and Architecture

What is Continuous ETL?

It is a process to Extract the data from Homogeneous or Heterogeneous data source then cleansed, enrich and Transform the data, Which is further Load back to Lake or Data Warehouse. It is a well-defined workflow and an ongoing process.ETL is a near to real-time process with the latency in seconds not in hours or days.

Working Architecture of ETL

The number of data sources can be there from where information is extracted. Then numerous transformations can be applied to it. There is a continuous process of Extract, Transform and Load as the data comes. The following steps are required to make the ETL process Continuous.

Extract Process Overview

  • Update notification – When the changes have been done helps to extract the data quickly. These types of systems are required for the continuous process.
  • Incremental extraction – Incremental extraction is supported by the systems which can provide the information about a record modification. When the system knows about record modification, it can extract the unread only.
  • Full extraction – Some systems are not able to provide the information about the last record changes, so reloading all the data is the only option. In these cases keeping track of the previous extraction is required. The continuous extraction is not preferable in this case as keeping track of the data is difficult in these kinds of systems.
  • Transform – Architecturally there are two ways to approach ETL transformation.
  • Multistage data transformation –In multistage data transformation the transformation occurs in the staging area before loading it to the warehouse.
  • In-warehouse data transformation – Data is extracted and loaded into the warehouse and transformations are done there.

Basic Transformations in ETL

  • Cleaning – Date format consistency, and mapping the data.
  • Deduplication – Remove duplicates values from the data.
  • Format revision – Date/Time conversions unit conversions.
  • Essential restructuring – Establishing key relationships across tables.

Advanced transformations

  • Derivation – Creating new data using existing data by applying business rules – for example, creating a revenue metric from taxes.
  • Filtering – Selecting only the specific row and columns from the data.
  • Joining – Adding data into one stream from multiple sources.
  • Splitting – Split the single columns into multiple columns.
  • Data Validation – Simple or complex data validation – for example, reject the rows from processing if the first three columns in a row are empty.
  • Aggregation – Aggregate data from multiple data sources and databases.
  • Integration – Standardize each unique data’s name with one standard definition.

Loading in ETL

Ordering – Keeping data accurate is a critical step of loading data as there can be deletion updation operations in the data pipeline which can lead to the wrong updation in the process.

Schemas evolution – What happens if data warehouse starts receiving wrong data type for a field that is expected be an integer. This situation can be destructive, so schema evaluation is there in the loading process to make sure everything occurs smoothly.

Monitorability – When there is a large number of data sources, failures are inevitable. The failure can occur due to the number of reasons.

  • Api’s downtime
  • Api’s expiration
  • Network congestion
  • Warehouse offline

Why Continuous ETL Matters?

The word ‘Data’ matters most here. Data is generating in every business, and the need for getting insights from the information is increasing. People want to track and query a variety of data sources, but the traditional model built for structured data was inadequate. So the conventional batch processing is too slow for making real-time decisions. So continuous ETL makes sense for ever-growing businesses who want to move their data and operations in real-time.


How to Adopt Continuous ETL Architecture?

When the real-time data is generating using sensors or applications, The continuous ETL can be applied to get insights and real-time alerts for detecting anomaly or suspicious security threats. The first step for Implementing the continuous ETL will be choosing the right kind of tools according to the use case and challenges. But before that data modeling and partitioning strategy can decide for storing and querying the data efficiently. The source-of-truth pipeline needed for feeding all data processing destinations. It should also serve as a real-time messaging bus and stateful stream processing.


Advantages of Enabling Continuous ETL Solutions

  • Updated Data Warehouses
  • Continuous & Real-time alerts and analytics
  • Continuous operations on the data as it comes
  • Enables low-latency data for time-sensitive analytics

Continuous ETL Best Practices

Analyzing data:- Analyzing the data will be the first step in the ETL best practice because having a mature data model will help in the long term.

Fixing data issues at source – The data issues should be set at the source end because the errors at the data source inevitably will affect the ETL system.

Efficient Partitioning – Based on the value of one or more columns, queries can be done more efficiently by reading only the relevant fraction of the total dataset.

Ensuring scalability – The scalability should be kept in mind when the solution is proposed. Because as the business grows, the system should be able to handle it gracefully.

Scheduling, Auditing & Monitoring ETL jobs – To ensure the ETL jobs are done as per decided, scheduling and monitoring are essential.


 Best Tools

Apache Kafka It serves as the real-time, scalable messaging bus for applications, with no EAI.

Amazon Kinesis Data Stream – Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service.

Amazon Kinesis Data Analytics – Analyze data, gain actionable insights in real time. It provides a serverless SQL query engine to join, aggregations over time windows, filters, and more.

Amazon Kinesis Firehose – It loads streaming data into data stores and configure a delivery stream to automatically convert the incoming data to columnar forms like Apache Parquet and Apache ORC.

Apache Flink – It support for stream and batch processing, sophisticated state management, event-time processing semantics, and exactly-once consistency guarantees for a state.

Apache Airflow Apache airflow is useful for scheduling ETL jobs monitoring and handling the jobs failures efficiently.


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