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ELT

Data Science Dojo is offering Airbyte for FREE on Azure Marketplace packaged with a pre-configured web environment enabling you to quickly start the ELT process rather than spending time setting up the environment. 

 

What is an ELT pipeline?  

An ELT pipeline is a data pipeline that extracts (E) data from a source, loads (L) the data into a destination, and then transforms (T) data after it has been stored in the destination. The ELT process that is executed by an ELT pipeline is often used by the modern data stack to move data from across the enterprise into analytics systems.  

 

ELT process
ELT process

 

In other words, in the ELT approach, the transformation (T) of the data is done at the destination after the data has been loaded. The raw data that contains the data from a source record is stored in the destination as a JSON blob. 

 

Airbyte’s architecture: 

Airbyte is conceptually composed of two parts: platform and connectors. 

The platform provides all the horizontal services required to configure and run data movement operations, for example, the UI, configuration API, job scheduling, logging, alerting, etc., and is structured as a set of microservices. 

Connectors are independent modules that push/pull data to/from sources and destinations. Connectors are built under the Airbyte specification, which describes the interface with which data can be moved between a source and a destination using Airbyte. Connectors are packaged as Docker images, which allows total flexibility over the technologies used to implement them. 

 

Obstacles for data engineers & developers  

Collection and maintenance of data from different sources is itself a hectic task for data engineers and developers. Building a custom ELT pipeline for all of the data sources is a nightmare on top that not only consumes a lot of time for the engineers but also costs a lot. 

In this scenario, a unified environment to deal with the quick data ingestions from various sources to various destinations would be great to tackle the mentioned challenges.  

 

Methodology of Airbyte 

 Airbyte leverages DBT (data build tool) to manage and create SQL code that is used for transforming raw data in the destination. This step is sometimes referred to as normalization. An abstracted view of the data processing flow is given in the following figure: 

Airbyte methodology
Airbyte methodology

 

It is worth noting that the above illustration displays a core tenet of ELT philosophy, which is that data should be untouched as it moves through the extracting and loading stages so that the raw data is always available at the destination. Since an unmodified version of the data exists in the destination, it can be re-transformed in the future without the need for a resync of data from source systems. 

 

Major features

Airbyte supports hundreds of data sources and destinations including:  

  • Apache Kafka  
  • Azure Event Hub  
  • Paste Data  
  • Other custom sources  

By specifying credentials and adding extensions you can also ingest from and dump to:  

  • Azure Data Lake  
  • Google Cloud Storage  
  • Amazon S3 & Kinesis  

Other major features that Airbyte offers: 

  • High extensibility: Use existing connectors to your needs or build a new one with ease. 
  • Customization: Entirely customizable, starting with raw data or from some suggestion of normalized data. 
  • Full-grade scheduler: Automate your replications with the frequency you need. 
  • Real-time monitoring: Logs all the errors in full detail to help you understand better. 
  • Incremental updates: Automated replications are based on incremental updates to reduce your data transfer costs. 
  • Manual full refresh: Re-syncs all your data to start again whenever you want. 
  • Debugging: Debug and Modify pipelines as you see fit, without waiting. 

 

What does Data Science Dojo provide?   

Airbyte instance packaged by Data Science Dojo serves as a pre-configured ELT pipeline that makes data integration pipelines a commodity without the burden of installation. It offers efficient data migration and supports a variety of data sources and destinations to ingest and dump data.  

Features included in this offer:   

  • Airbyte service that is easily accessible from the web and has a rich user interface. 
  • Easy to operate and user-friendly. 
  • Strong community support due to the open-source platform. 
  • Free to use. 

 

Conclusion  

There are a ton of small services that aren’t supported on traditional data pipeline platforms. If you can’t import all your data, you may only have a partial picture of your business. Airbyte solves this problem through custom connectors that you can build for any platform and make them run quickly. 

Install the Airbyte offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy Airbyte for FREE by clicking below:

CTA - Try now 

January 27, 2023

Data Science Dojo is offering DBT for FREE on Azure Marketplace packaged with support for various data warehouses and data lakes to be configured from CLI. 

 

What does DBT stands for? 

Traditionally, data engineers had to process extensive data available at multiple data clouds in the same available cloud environments. The next task was to migrate the data and then transform it as per the requirements, but Data migration was a task not easy to do so. DBT short for Data Build Tool, allows the analysts and engineers to manipulate massive amounts of data from various significant cloud warehouses to be processed reliably at a single workstation using modular SQL. 

It is basically the “T” in ELT for data transformation in diverse data warehouses. 

 

ELT vs ETL – Insights of both terms

Now what do these two terms mean? Have a look at the table below: 

 

ELT 

ETL 

1.  Stands for Extraction Load Transform  Stands for Extraction Transform Load 
2.  Supports structured, unstructured, semi structured and raw type of data  Requires relational and structured dataset 
3.  New technology, so it’s difficult to find experts or to create data pipelines  Old process, used for over 20 years now 
4.  Dataset is extracted from sources and warehoused in the destination and then transformed  After extraction, data is brought into the staging area where’s its transformed and then loaded into target system 
5.  Quick data loading time because data is integrated at target system once and then transformed  Takes more time as it’s a multistage process involving a staging area for transformation and twice loading operations 

 

Use cases for ELT 

Since dbt relates closely to ELT process, let’s discuss its use cases: 

  • Associations with huge volumes of information: Meteorological frameworks like weather forecasters gather, examine and utilize a lot of information consistently. Organizations with enormous exchange volumes additionally fall into this classification. The ELT process considers faster exchange of data 
  • Associations needing quick accessibility: Stock trades produce and utilize a lot of data continuously, where postponements can be destructive. 

 

Challenges for Data Build Tool (DBT)

Data distributed across multiple data centers and the ability to transform those volumes at a single place was a big challenge. 

Then testing and documenting the workflow was another problem. 

Therefore, an engine that could cater to the multiple disjointed data warehouses for data transformation would be suitable for the data engineers. Additionally, testing the complex data pipeline with the same agent would do wonders. 

Working of DBT

Data Build Tool is a partially open-source platform for transforming and modeling data obtained from your data warehouses all in one place. It allows the usage of simple SQL to manipulate data acquired from different sources. Users can document their files and can generate DAG diagrams thereby identifying the lineage of workflow using dbt docs. Automated tests can be run to detect flaws and missing entries in the data models as well. Ultimately, you can deploy the transformed data model to any other warehouse. DBT serves pleasantly in the cutting-edge information stack and is considered cloud agnostic meaning it operates with several significant cloud environments. 

 

Analytics engineering DBT

(Picture Courtesy: https://www.getdbt.com/

 

 Important aspects of DBT

  • DBT enables data analysts with the feasibility to take over the task of data engineers. With modular SQL at hand, analysts can take ownership of data transformation and eventually create visualizations upon it 
  • It’s cloud agnostic which means that DBT can handle multiple significant cloud environments with their warehouses such as BigQuery, Redshift, and Snowflake to process mission-critical data 
  • Users can maintain a profile specifying connections to different data sources along with schema and threads 
  • Users can document their work and can generate DAG diagrams to visualize their workflow 
  • Through the snapshot feature, you can take a copy of your data at any point in time for a variety of reasons such as tracing changes, time intervals, etc. 

 

What Data Science Dojo has for you 

DBT instance packaged by Data Science Dojo comes with pre-installed plugins which are ready to use from CLI without the burden of installation. It provides the flexibility to connect with different warehouses, load the data, transform it using analysts’ favorite language – SQL and finally deploy it to the data warehouse again or export it to data analysis tools. 

  • Ubuntu VM having dbt Core installed to be used from Command Line Interface (CLI) 
  • Database: PostgreSQL 
  • Support for BigQuery 
  • Support for Redshift 
  • Support for Snowflake 
  • Robust integrations 
  • A web interface at port 8080 is spun up by dbt docs to visualize the documentation and DAG workflow 
  • Several data models as samples are provided after initiating a new project 

This dbt offer is compatible with the following cloud providers: 

  • GCP 
  • Snowflake 
  • AWS 

 

Disclaimer: The service in consideration is the free open-source version which operates from CLI. The paid features as stated officially by DBT are not endorsed in this offer. 

Conclusion 

Incoherent sources, data consistency problems, and conflicting definitions for measurements and enterprise details lead to disarray, excess endeavors, and unfortunate data being dispersed for decision-making. DBT resolves all these issues. It was built with version control in mind. It has enabled data analysts to take on the role of data engineers. Any developer with good SQL skills is able to operate on the data – this is in fact the beauty of this tool. 

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. Therefore, to enhance your data engineering and analysis skills and make the most out of this tool, use the Data Science Bootcamp by Data Science Dojo, your ideal companion in your journey to learn data science! 

Click on the button below to head over to the Azure Marketplace and deploy DBT for FREE by clicking on “Get it now”. 

 Try now - CTA

Note: You’ll have to sign up to Azure, for free, if you do not have an existing account. 

September 29, 2022

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