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@surfalytics

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Post #47 312
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Post #46 241
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Post #45 258
#Surfalytics launched premium subscription with the cost of Netflix where you will get access to the study materials, almost support in your career grow , helping with job searching and many other sweet perks. But the most important, every month we will launch two dedicated project that will upskill you and add portfolio. One for entry level folks on #BusinessIntelligence and Data Analytics track and another one more advance on #DataEngineering track.

Early price 20CAD per month is available.
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Post #43 289
The success of your career often depends on your impact. While you might find yourself building numerous things—such as reports, pipelines, ingesting additional data sources, merging pull requests, or producing many lines of code—you may realize that these activities alone aren't propelling your career forward.

You're producing outputs and gauging your work by these outputs. However, output isn't synonymous with outcome. Your outputs might have limited business value and negligible impact. In essence, you're caught in the "building trap."

This is why I highly recommend the book "Escaping the Build Trap: How Effective Product Management Creates Real Value." This book will introduce you to the fundamentals of product management and the outcome-driven approach. It aims to help you avoid the building trap, create value for businesses, and focus on meaningful impacts that can genuinely advance your career.

P.S. Naturally, this will shine brightest when paired with a team and leadership that truly have their eyes on outcomes and can deftly distinguish between "output" and "outcome." Wouldn't that be refreshing?

Link to book: https://www.goodreads.com/book/show/42611483-escaping-the-build-trap
Goodreads Escaping the Build Trap: How Effective Product Manageme… To stay competitive in today’s market, organizations ne…
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Post #39 424
Are you planning to move from analyst role to the data engineering role and don't know where to start? I bet for any questions out there, there is ideal book that exists and this question is not an exception.

The "The Missing  README" is the best book for anyone looking for the foundational software engineering knowledge. Even you don't plan to work as a data engineer right now, you can still learn basic concepts and communicate effectively with backend engineer team.

Personally, this book has helped me tremendously in my career and I highly recommend it to anyone who are lacking Computer Science degree 🤗

Book link: https://lnkd.in/dQxNe3dm
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Post #35 355
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Post #34 326
Enjoy time at work🤙
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Post #32 313
From rockyourdata.cloud: "We have awesome news! We launched education programs for Data Engineer, Data Analysts and BI engineers positions. We are going to utilize years of experience into our curriculum and help people move to data industry and land first job"

Rock Your Data is North America consulting company with focus on Cloud Analytics.

Please share https://www.linkedin.com/posts/rock-your-data_dataengineer-dataanalyst-biengineer-activity-7118664122300321792-mXWV
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Post #31 276
Only remote!
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Post #29 237
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Post #28 314
🌟 Parquet:
Advantages: Columnar, compressed, schema evolution support!
Disadvantages: Not for write-heavy workloads.
Use Cases: Analytical querying & data warehousing.

🌟 Avro:
Advantages: Row-based, schema evolution, efficient serialization.
Disadvantages: Slower for analytical queries.
Use Cases: Data serialization & data interchange.

🌟 JSON:
Advantages: Human-readable & schema flexible.
Disadvantages: Inefficient storage.
Use Cases: Web data interchange & configuration.

🌟 DeltaLake:
Advantages: ACID Transactions, schema enforcement.
Disadvantages: Proprietary.
Use Cases: ACID transactions & schema enforcement in Data Lakes.

🚀 Tips for Maximizing Benefits in
#Spark:
- Choosing Format: Select data format based on read-write patterns, query performance, and storage efficiency.

- Partitioning: Properly partition data to optimize read performance, especially for large datasets.

- Compression: Choose an appropriate compression codec considering the trade-off between storage space and CPU usage.

- Caching: Leverage Spark’s caching features for frequently accessed datasets.

- Schema Evolution: Design schemas thoughtfully to allow for evolution over time without causing data inconsistency or requiring expensive migrations.
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Post #26 259
Is university degree important for data jobs? Not at all. No one cares what degree you have. Skills are more important.

Today I talked with colleague, who paid 50k in 3rd tier US university for 1 year of Masters in Business Analytics + cost of living for 1 year. Overall 80k money waste. Yes she got the job and some skills but in what cost. With the right focus and content, she would "fake it and make it" in 4-5 months. Imagine degree for 2 years and 1st or 2nd tier university with cost of living🫨
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Post #25 260
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Post #24 280
Anything stops you from success?!🏖
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