Any data engineer should know the terms MPP and SMP.

Let me share a story about laundromats that will help you to remember this forever.

But first the theory.

SMP - Symmetric Multi-Processing
● Traditionally one server systems
● Data stored locally
● Processors share single OS, memory, I/O devices
● Scale-up only - physical limitations to scaling to accommodate workload

MPP - Massively Parallel Processing
● Multi-node(server) systems
● Data stored externally
● Scale-out - add more Compute nodes, each with
dedicated CPU, memory & I/O subsystems
● No single point of contention

Examples of SMP are SQL Server, MySQL, Postgres.
Examples of MPP are Redshift, Synapse Dedicated Pool, BigQuery, Snowflake.

The bottom line, that usually for data engineering projects we are going to utilize the MPP to handle big volume of data in distributed method. By the end of the day, it depends on requirements and it is the role of DE to make the right call.

The concept of using washing machines as an analogy isn't originally mine; it's borrowed from an age-old Teradata study guide. However, I find it quite appealing. This metaphor effectively illuminates the function and method of each approach. Plus, it's a memorable way to understand these concepts.

Let's imagine that two friends have plans for the Friday evening and they both have a mountain of laundry to do.

Sam decides to wait until Saturday to do their laundry. They believe using one large machine at the laundromat will be sufficient. On Saturday, Sam heads to the laundromat, only to find that the single large machine takes much longer to complete the task. Their entire day gets consumed by laundry, eating into their relaxation time.

Max, on the other hand, goes to the laundromat on Friday evening, before the party. They use multiple smaller machines simultaneously, dividing their laundry among them. This parallel approach allows Max to finish the laundry quickly, saving them enough time to enjoy the party and have a free weekend.

Sam, seeing how Max managed to save time and still enjoy the weekend, realizes the efficiency of parallel processing. While the single machine is powerful, it's not always the most time-efficient choice for large tasks.

When it comes to scalability, SMP is known for vertical scale or scale up. MPP is known for horizontal scale and scale up.

Another important term for MPP is "shared nothing" architecture - means each node has own set of CPUs, RAM, drive.

All this lead us to common data engineering problems like data skew, data distribution, I/O, network traffic, shuffling and so on.

I hope you learn a thing and will impress your next hiring manager!

PS Despite the fact that Teradata missed the opportunity for cloud analytics we can learn about another interesting term - "vendor lock", you may ask S&P500 companies who are still Teradata customers.

PS Likes are welcome -> https://www.linkedin.com/posts/dmitryanoshin_mpp-teradata-snowflake-activity-7135674781571436544-y8cS

What is Surfalytics?

Sharing Nothing Architecture one is key term in dataengienering and distributed computing. It was coined several decades ago by Michael Stonebraker and was used in Teradata's 1983 database system.

Shared Nothing Architecture (SN) is a computing setup where each task is handled independently by separate units in a computer network. This method avoids delays and conflicts common in "shared everything" systems, where multiple units may need the same resources simultaneously.

SN systems are reliable; if one unit fails, others continue unaffected. They're easily scalable by adding more units. In databases, SN often involves 'sharding', splitting a database into smaller sections stored separately.

PS post for like https://www.linkedin.com/posts/dmitryanoshin_dataengienering-activity-7136413923381059585-rvlW

The primary question for every data professional out there is: How will Generative AI and LLMs reshape the industry, and what are the expectations for future data professionals?

The answer depends on two opposing options:
1. AI will replace roles like Data Engineer, BI Analyst, Data Scientist, and so on.
2. AI will complement these roles, enabling people to work more efficiently, with higher quality and significant impact.

Whichever option you choose, you’ll agree that a growth mindset and constant learning are key to staying competitive and being ready to pivot your career and pick up the right skills.

Our careers remind me of an underground subway escalator. While it’s going down, you’re moving up, step by step. You may falsely assume that you’ve reached the top, but forget that the escalator is constantly going down.
The bottom line is, as soon as you stop learning and growing, you de facto degrade and lose market value.

At the Surfalytics community, my primary objective is to stay up-to-date with modern directions in the industry, talk with people globally, and move in the same direction.

I feel a wave of power, energy, and momentum that will bring everyone to the right destination, saving them from wasting money and time. On the same note, I feel blessed to see how people are changing their lives forever.

PS On the picture Tofino, BC! Every summer we run Surf + Data bootcamp out there!

Link for likes;) https://www.linkedin.com/posts/dmitryanoshin_dataengineering-analytics-dataanalyst-activity-7137165815346315264-iKWk

https://github.com/will-stone/browserosaurus#readme - help to control multiple browsers

The most straightforward yet profound question for newcomers in data engineering is: What is the difference between ETL and ELT?

You can work with tools like dbt and data warehouses without actually considering the difference, but understanding it is crucial as it leads to the right tool choice depending on the use case and requirements.

You might think of ETL as an older concept, from the time when data warehouses were used primarily for storing the results of the Transformation step. This required a powerful ETL server capable of processing the same volume of data, ready to read each record and every row in a table or file. With large volumes of data, this could be expensive.

However, with the rise of Cloud and Cloud Data Warehousing, the need for powerful ETL compute has diminished. Now, we can simply COPY data into cloud storage and then into the cloud data warehouse. After this, we can leverage the powerful compute capabilities of distributed cloud data warehouses or SQL engines.

The advent of cloud computing wasn't the only pivotal moment. Even before the cloud, ETL tools like Informatica employed a 'push down' approach, pushing all data into MPP data warehouses like Teradata, and then orchestrating SQL transformations.

Let's consider a simple example:

In the case of ETL:
1. Extract Orders and Products data.
2. Transform the data (join, clean, aggregate).
3. Load the data into the data warehouse, often using INSERT (a slower, row-by-row process).

In the case of ELT:
1. Extract Orders and Products data.
2. Load the data into the data warehouse, often using COPY for storage accounts and data warehouses (a faster, bulk load process).
3. Transform with SQL or tools like DBT or Dataframes.

Reflecting on the role of Spark, it becomes clear that Spark is an actual ETL tool since it reads the data when performing transformations.

Link to share to like: https://www.linkedin.com/posts/dmitryanoshin_etl-elt-dataengineering-activity-7137583690678747136-7imR

Say No to "Season of Yes".

I know some of you have challenges due to lack on knowledge of Cloud Computing - Azure, AWS, GCP.

Starting January I will run 6 weeks program online in University of Victoria. Tuesday/Thursday 6pm PST for 2 hours.

The price is 715CAD. Money is going to university, I am not getting much. But it is extremly great opportunity to close the gap in cloud computing, Azure/AWS and do lots of hands on.

You employer may pay for this course.

Highly recommend. I think 10 seats left.

https://continuingstudies.uvic.ca/data-computing-and-technology/courses/cloud-computing-for-business/

Last weekend, we worked on a traditional data engineering project at Surfalytics, which involved using Snowflake as a Data Warehouse, dbt for transformations, and Fivetran for data ingestion from Google Drive.

For BI and data exploration, we utilized Hex. We hosted dbt in a container and ran it via GitHub Actions.

The project was prepared and executed by Nikita Volynets, and Tsebek Badmaev did an amazing job documenting the code in GitHub. Now, anyone can reproduce it and learn from it.

I bet everyone learned something new and will use this newfound knowledge at work or in interviews.

Link to the repo: https://lnkd.in/g4PXNV_W

Link for like: https://www.linkedin.com/posts/dmitryanoshin_dataengineering-dbt-snowflake-activity-7137879439664693248-V1wzp

Before starting chasing analytics and data engineering jobs I usually suggest to be more or less fluent with multiple things:

- CLI: popular commands, navigation, vim and nano text editors, permissions, environment variables
- GitHub (or any similar platform) with focus on Code Reviews, PRs, development lifecycle, basic pre-commit, CI/CD
- Containers: docker file, image, compose
- IDE of your choice: don't know where to start? Take Visual Code.

You don't need to be pro in any of these but it will make a difference and pay back in long term i.e. #engineeringexcellence

Last week at Surfalytics I ran CLI and GitHub and next Saturday planning to wrap containers.

All of this will wrap into the 3 simple free courses - "Just enough <TERM> for data professional"

Link for like: https://www.linkedin.com/posts/dmitryanoshin_engineeringexcellence-dataengineering-analyticsengineering-activity-7138073576309489667-_H2h