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Summary
Big-data-integration-platforms are systems that help organizations connect, combine, and share massive amounts of data across different technologies and cloud services, making it easier for teams to work with information from multiple sources. These platforms allow companies to manage data from various tools and environments, helping drive better decisions and innovation.
Assess needs: Take time to review your current data sources and workflows to identify which integration features are most important for your business goals.
Explore interoperability: Look for platforms that make it simple to share and access data between popular services like Snowflake, Databricks, and Microsoft Fabric without extra replication or delays.
Stay current: Regularly research new technologies and services—such as those on Google Cloud Platform or featured in industry buyer guides—to keep your team’s data integration approach up to date.
I’ve just published a new article exploring strategies to unify data sharing across Snowflake, Databricks, and Microsoft Fabric. While consolidating onto a single platform is often ideal, the reality for many large enterprises is more complex. Team autonomy, legacy investments, and strategic diversification often lead to multi-cloud and multi-product environments. Can your cross-platform integration architecture become a strategic advantage? The article focuses on options to share delta parquet and iceberg format storage amongst the three platforms:
https://lnkd.in/gs4nS8Tt
In the real world, very few large organizations are unified on a single data and analytics platform. Snowflake, Databricks, and Microsoft Fabric are all very popular products with widespread adoption. All three offer lakehouse architecture tools, but what are your options if you have data in more than one of these products? How do you share data amongst the platforms in a way that minimizes replication, is cost efficient, and has low latency?
This post is the first in a three-part series focusing on interoperability amongst Snowflake, Databricks, and Microsoft Fabric.
#Snowflake#Databricks#AzureDatabricks#MicrosoftFabric
Partner, Chief Software Analyst leading the software research and advisory team to enable enterprises, service and software providers to reach their maximum potential in the AI enabled world.
🌟 From Hadoop & Big Data to Data Engineering on GCP 🌟
As Data Engineers, we play a vital role in enabling data-driven decision-making. Here’s a quick overview of what we typically do:
✅ Manage data ingestion from diverse sources.
✅ Build batch pipelines.
✅ Develop streaming pipelines.
✅ Create ML and LLM pipelines.
Now, what technologies or services do we use to achieve this on GCP? Let’s break it down:
What are the technologies or services we use on Google Cloud Platform (GCP)?
• For ingestion: GCP offers Cloud Data Fusion and Cloud Composer for ETL workflows. For real-time ingestion, Pub/Sub is a popular choice. Many organizations also use third-party tools like Informatica, Talend, or Fivetran. For API-based ingestion, Cloud Functions provides a serverless solution.
• For batch processing: Cloud Dataflow, based on Apache Beam, is a key service for scalable batch data processing. GCP also supports Dataproc, which simplifies Spark and Hadoop-based workflows on the cloud.
• For stream processing: GCP excels in stream processing with Pub/Sub and Dataflow. Pub/Sub handles real-time messaging, while Dataflow processes the streaming data with its unified batch and stream processing capabilities.
• For machine learning: Vertex AI is the flagship platform for developing and deploying machine learning models on GCP. For exploratory data analysis and BI workflows, BigQuery ML provides integrated machine learning capabilities directly within BigQuery.
• For data warehousing: BigQuery is GCP’s serverless data warehouse, offering high-performance analytics at scale. Its deep integration with other GCP services and SQL interface makes it a favorite among data engineers.
• For visualization: GCP integrates seamlessly with Looker and third-party tools like Tableau and Power BI. Looker, in particular, provides advanced data exploration and visualization capabilities.
• For orchestration: GCP relies on Cloud Composer (built on Apache Airflow) for orchestration, providing a powerful tool to manage data pipelines and workflows effectively.
In short: In today’s Data Engineering world, the key skills on GCP are SQL, Python, BigQuery, Dataflow, Dataproc, Pub/Sub, Vertex AI, Cloud Composer, Cloud Functions, and Looker. Start with SQL, Python, BigQuery, and Dataflow and build on additional services as required by the role.
💡 “As Data Engineers, our role extends beyond tools—it’s about designing scalable and efficient pipelines that unlock the true potential of data. Staying updated with GCP’s innovations is essential for success in this dynamic field.”
👉 Follow Durga Gadiraju (me) on LinkedIn for more insights on Data Engineering, Cloud Technologies, and the evolving world of Big Data on GCP!
#GCP#DataEngineering#SQL#Python#BigData#Cloud