𝐌𝐚𝐫𝐜𝐞𝐥

A human with REALITIES || Tech(DataEng)

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

guide.

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

We don’t give enough credits to producers

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

Lately, I don’t let unnecessary things overwhelm me

[27. @dimvji]

never the type to try fit in, i’d sit alone if i have to

[LEYE @leyeConnect]

And don’t wear them only for special occasions.

You are an occasion, you are special, wear them whenever and to wherever you want.

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

Overthinking things with little to no negative consequence is not living, it’s death.

Over-thinkers will never take risks, or move a step forward, or when they do, it might’ve already been too late.

Sometimes just FAFO, as long as it’s a harmless decision. Just LIVE!

[David @Justtdavid_]

texting everyday really does create a false sense of closeness

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

Every human has surely experienced or will experience a “you can’t eat your cake and have it too” moment

[⚭ @lekimgym]

Enjoy being while becoming

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

@TrendingEx @dubemtheking

[🍂 @Lovandfear]

“I want to talk about everything with at least one person the way I talk about things with myself.”

— Fyodor Dostoevsky

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

Patterns! Never fail to notice patterns

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

A less common way to express love is by praying for God to be present in the lives of those you care about.

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

I thank God for my life everyday, I am grateful Lord

[millar🏂 @millar_forever]

I have decided to enter the tunnel with my own light

[DataVidhya @thedatavidhya]

> Disappear for 6–8 months and Crack Data Platform Engineer Roles.

Here is how you can do it in 6 steps:

Step 1 (3–4 Weeks) → Strong Foundations

→ Advanced SQL (CTEs, window functions, optimization)
→ Python (data pipelines, APIs, scripting)
→ Linux & shell scripting
→ Basics of distributed systems

Step 2 (4–5 Weeks) → Data Warehousing Core

→ Star & Snowflake schema design
→ Partitioning & clustering
→ Query optimization
→ Columnar storage concepts

Step 3 (4–5 Weeks) → ETL Frameworks

→ Build reusable ETL pipelines
→ Airflow (DAG design, retries, scheduling)
→ Data validation & quality checks
→ Incremental data loading

Step 4 (5–6 Weeks) → Big Data Processing

→ Apache Spark (DataFrames, transformations)
→ PySpark optimization techniques
→ Handling TB-scale data
→ Performance tuning (caching, joins)

Step 5 (4–5 Weeks) → Data Platform Design

→ Data lakes vs data warehouses
→ Lakehouse architecture (Delta Lake/Iceberg)
→ Metadata management
→ Schema evolution & versioning

Step 6 (4–6 Weeks) → Production & Cloud

→ AWS (S3, EMR, Redshift, Glue) or GCP stack
→ Docker + basic Kubernetes
→ CI/CD for data pipelines
→ Monitoring (logs, alerts, failures)

Execution Layer (parallel throughout)

→ Build a mini data platform (ingestion → processing → serving)
→ Integrate batch + streaming pipelines
→ Work with real datasets (events, logs, APIs)
→ Document architecture + decisions (GitHub + diagrams)

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

Christ is risen, your sins are forgiven, shalom ✝️❤️

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

Or apply for DataFellow’s https://t.co/MGLrA7XgJt

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

For any Data skill, apply for this DataCamp scholarship https://t.co/bj8wConoOI

[𝐌𝐚𝐫𝐜𝐞𝐥 @0robosa]

@CoreDataEngr Whatever it is, I hope not to miss it