DATA ENGINEERING

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DATA ENGINEERING

DATA ENGINEERING

@dataengwithaws

upgrade your knowledge in DATA ENGINEERING with A W S

Katılım Ağustos 2023
63 Takip Edilen2.8K Takipçiler
DATA ENGINEERING
DATA ENGINEERING@dataengwithaws·
Deploy OpenClaw with Ollama, Kimi K2.5 (via OpenRouter), and Claude (via Anthropic API/subscription) on an Oracle Cloud Always-Free ARM instance, connected to Telegram. github.com/akrish1982/ope…
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DATA ENGINEERING
DATA ENGINEERING@dataengwithaws·
@thedatavidhya I completely agree! Using AWS Glue for ETL tasks has been a game-changer for our team. It simplifies the process and allows us to focus on more complex data engineering challenges.
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DataVidhya
DataVidhya@thedatavidhya·
AWS Data Engineering in plain English: • S3 → data lake storage • Glue → ETL, serverless Spark jobs • Athena → run SQL directly on S3 • Redshift → data warehouse for analytics • Kinesis → real-time streaming • MSK (Managed Kafka) → Kafka on AWS • EMR → big data clusters (Spark, Hive, Presto) • RDS / Aurora → relational databases • DynamoDB → NoSQL database • Neptune → graph database • OpenSearch → search & log analytics Pipeline & Orchestration: • Step Functions → workflow orchestration • Lambda → event-based automation • EventBridge → event routing between services • Data Pipeline → legacy orchestration tool (still used in some stacks) Monitoring & Governance: • CloudWatch → monitoring, logging, alerts • CloudTrail → audit logging • Lake Formation → manage permissions in data lakes • Glue Data Catalog → central metadata store Integration & Security: • IAM → access & permissions • VPC → secure networking • Secrets Manager → manage database/API credentials That’s 90% of what you’ll touch day-to-day as a Data Engineer.
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KodeKloud
KodeKloud@KodeKloudHQ·
AWS Athena explained in 120 seconds 💡 Run SQL queries directly on your S3 files without setting up any servers. Just point Athena to your data, write standard SQL, and get instant results. Pay only for what you scan. #AWS #Athena #DataEngineering #CloudComputing #Serverless
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Nikola Ilic
Nikola Ilic@DataMozart·
No better way to kick off the weekend than reading through the feedback for my two sessions from Power Conference Prague last week😊 My full-day #MicrosoftFabric precon got a 4.8 average rating, whereas the regular #OneLake #DataEngineering session was rated 4.9! Thank YOU🙏
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Data Science Council of America
Data Science Council of America@DASCA_Insights·
Data Scientists vs. Data Engineers— while both roles are essential to a successful data ecosystem, understanding the distinction between them is often overlooked, yet crucial for designing effective, scalable, and intelligent data solutions. #DataScience #DataEngineering #DASCA
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Moshood
Moshood@legit_demosh·
Preparing for my Data Engineering training next week. Some key terms I’ve learned: Data Pipeline, ETL/ELT, Data Warehouse, Data Lake, Batch vs Streaming, Schema, Data Quality, Orchestration, Scalability. Learning concepts > chasing tools. #DataEngineering #LearningInPublic
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Virendra Codes
Virendra Codes@VirendraCodes·
Just published a new blog ✍️ Netflix isn’t just about streaming movies; it’s about streaming data at a massive scale. From real-time tracking to recommendations and A/B testing, I broke it all down for beginners 👇 🔗 @vtambavekar239/netflix-data-engineering-explained-for-beginners-tools-architecture-and-real-world-use-cases-28594ea7b924" target="_blank" rel="nofollow noopener">medium.com/@vtambavekar23#DataEngineering #BigData #Netflix
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Chris Perry
Chris Perry@ChrisMPerry·
I'm currently working on a new video to be released in the first week or so of January. Here's a sneak peek of the thumbnail. While this video's focus is on "flows", the concepts covered will apply to any pipeline created by BI tools. #Tableau #DataEngineering #DataAnalytics
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