A curated list on Data Engineering
Published: 2022-02-01 08:09:24

Catch up on the trending articles from Data Engineering Space.


A curated list of the most engaging blog posts will be shared here and a newsletter will be sent out to keep the readers up-to-date with the world of Data Engineering.

Data is the epicenter of the Digital world. Every byte of data has a story to tell.

The True business value lies in a well-narrated story. To achieve this, data engineers should pre-plan, design, develop and deploy data pipelines carefully.

Companies collect and analyze vast amounts of data as their success and growth depend on it.

Only 30% of the data is deriving meaningful insights. The rest of it is being mounted unproductively on a remote storage device. We can only leverage the true value of these datasets when they are well arranged and streamlined for accessibility and ease of use.

Handling and managing data is not as easy as it sounds. With the efficient design, the system can derive valuable insights.

Data Engineering to the rescue.

Data Engineering is a brilliant and rewarding approach to get maximum value out of your data by carefully organizing, curating, and streamlining the data end-to-end.

Data Engineering has a lot to offer towards achieving data-centric business requirements. Companies are adapting Data Engineering excessively and focusing on implementing it in every business use case to make the data speak.

A good start to know how rewarding Data Engineering is to refer to what the Experts in the Data field are predicting about the data trends and how the future will be. this post might change your overall perspective of the field,

[5 Big Data Experts Predictions For 2022
](https://medium.com/coriers/5-big-data-experts-predictions-for-2022-a333111299ed)

Thoughts and Opinions in the blog post are from the Data startup founders and Data Engineers contributing to fast-paced Data-Centric companies.

It’s hard to predict which technologies will leave their mark in the Data sector and which ones will become a part of history.

Adding new features to cope with challenging and changing businesses will make the technologies adapt and grow. Experts highlight which technologies are achieving this and how they will be contributing to the greater good.

It must be well established by now about how important Data Engineering will be for companies.

Our next post is about how teams can leverage cloud technologies for collaboration and productivity.

SQL- the de facto standard data querying language. Most business operations that are data-centric heavily rely on general SQL querying.

[How to share SQL queries in Amazon Redshift with your team
](https://dev.to/aws-builders/how-to-share-sql-queries-in-amazon-redshift-with-your-team-6lh)

This post explains how remote teams can share work over the cloud with team members and how the tasks can be fulfilled by delegating the work.

The content is about Amazon Redshift, a Cloud Data Warehouse, and SQL, with hands-on illustration that is well structured.

Reading and writing data is achieved with simple SQL queries for a long time. Data querying was a crucial and integral part of the business when the main focus was descriptive analytics(generating reports).

Over time business requirements extended to fulfill challenging business requirements data needed to be curated and aggregated to a final agreed-upon version. This final version can be utilized for analytics by Business Intelligence teams.

We can derive meaningful insights by applying advanced transformations to the datasets.

Data transformation is a must and heavily applied operation in any ETL, ELT job, and is implemented in almost all business use cases ranging from very simple to high-level projects.

When considering cost and performance, business requirements demand different strategies.

Data Transformation happens at two stages in a Data Pipeline, before and after loading to reliable storage. Former is when we extract the data from the source, transform and load it to the destination(ETL), while in the latter we extract data from the source, load it to the destination and then perform transformations on the destination datasets(ELT).

ETL strategy can be optimal to transform small datasets in memory.

When the dataset is large, applying transformation in-memory is no longer a viable option as it requires spinning up many master and worker nodes in the cloud. This approach can be time-consuming and results in outcomes with high latency by affecting the cost to compute.

ELT can be very rewarding when the requirement is to apply transformations on large datasets to reduce operational costs.

[Extract csv data and load it to PostgreSQL using Meltano ELT
](https://dev.to/zompro/extract-csv-data-and-load-it-to-postgresql-using-meltano-elt-4ipf)

In this blog post, you will learn how to perform ELT with an exciting DataOps Framework “Meltano” and work on PostgreSQL, a high-demand relational SQL language with python.

It just doesn’t stop there. Data Engineering is not just extracting data, transforming for meaningful insights, and loading it to a reliable storage unit.

We have to bring together multiple pieces of the puzzle to make the data journey possible. one piece is designing data pipelines for the data movement from source to destination.

[Data Engineering Pipeline with AWS Step Functions, CodeBuild and Dagster
](https://dev.to/tkeyo/data-engineering-pipeline-with-aws-step-functions-codebuild-and-dagster-5290)

This blog post explains how an end-to-end data pipeline is built to collect, process, and visualize data on the cloud.

A workflow is a unit of work that has a sequence of actions.

A workflow is designed to function in a repeatable fashion, triggered by a pre-defined schedule or events.

After a workflow is triggered, each action in the workflow needs monitoring. Monitoring should be set up and configured to store the state of every single workflow action in the form of logs for each pipeline run.

The operations team will be alerted if any action fails to implement corrective actions. Dagster is a workflow management platform similar to airflow, which orchestrates your Data Engineering tasks for machine learning, analytics, ETL and comes with an event scheduler that handles failures during unlikely events and helps in monitoring the state by sending out notifications and logs to the team.

AWS Step Functions is a low-code, visual workflow service designed to build distributed applications by automating IT and business processes and building data + machine learning pipelines.

Distributed applications are suitable for delivering performance boosts and resilience to the overall system and are in high demand.

Here, you get to learn how to write distributed applications for parallel processing and high performance on the cloud using AWS step Functions.

Handling data variety often seems a challenging endeavor. Data today comes in different types and formats that can be stored and used according to business requirements. When I say storage, that doesn’t just mean a traditional database. Data storage comes in different shapes and sizes, from on-premise enterprise storage to cloud storage.

Depending on the type, data lakes are viable for storing unstructured data and are suitable for Data science-related tasks.

Data warehouses are for structured and semi-structured data. Data Warehouses stores data from ETL jobs and are used for analytics by Business Intelligence teams to derive meaningful business insights.

Cloud data lakes are in high demand. Delta sharing is one of a kind Azure service to manage and handle your data lakes.

[Azure Synapse — How to use Delta Sharing?
](https://medium.com/@wahidatoui/azure-synapse-how-to-use-delta-sharing-f9f76e5083b7)

In this blog post, you will learn about Delta sharing and how to use it for your business needs.

Delta sharing is a limitless analytics service designed to bring together enterprise data warehousing and Big Data analytics.

Automation is one of the must-implement features if the workflow has repetitive tasks to minimize resource utilization.

In Data Pipelines, one of the most common tasks is data movement from source to destination. The same operations get applied for different use cases.

Snowflake is a Trending and Popular Cloud Data warehouse offering easy to store and process data interface. Snowflake provides integrations of tens of different libraries and languages to expand your business use cases based on underlying technologies. Amazon Simple Storage Service(S3) is the most popular and excessively adapted cloud object storage in the market.

Knowing how to use and implement Snowflake to automate the data movement can be an immense advantage to your business.

[Automating Data Movement from Snowflake to S3
](https://selectfrom.dev/automating-data-movement-from-snowflake-to-s3-b280b1ca7f28)

This post might be a good start for you to learn how to automate your data flows from the cloud data warehouse to reliable cloud storage.

Opinions are my own. Please leave a comment.

Until next time.

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