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ClickHouse Inc. uses ClickHouse Cloud for data warehousing 💾 Learn how below 👇

Firebolt and Snowflake are both cloud-based data warehousing platforms, but they differ in several key aspects: Below is a 7 way compare 1) Architecture Snowflake uses a shared data architecture where data is stored centrally and compute resources are provisioned on-demand. Firebolt, on the other hand, employs a decoupled storage and compute architecture with advanced indexing technology for faster query performance 2) Performance Firebolt's indexing technology allows for sub-second query speeds, even on petabyte-scale datasets, outperforming Snowflake in terms of raw query performance.Snowflake's performance is optimized for concurrency, enabling multiple users to run queries simultaneously without impacting performance 3) Scalability Both platforms offer scalability, but they differ in their approach. Snowflake scales compute and storage independently, while Firebolt's auto-scaling capabilities dynamically adjust compute resources based on workload demands 4) Pricing Snowflake offers a usage-based pricing model, where users pay for compute and storage resources consumed. Firebolt also has a consumption-based pricing model but promises cost savings through optimized resource utilization and minimized data storage requirements due to its indexing technology 5) Data Ingestion and Transformation Snowflake supports a wide range of data sources and provides built-in transformation capabilities. Firebolt has built-in connectors for popular data sources and a powerful transformation engine.[2] 6) Security and Compliance Both platforms offer robust security features, including data encryption, access controls, and compliance with industry standards like HIPAA and GDPR.[4] 7) Integration and Ecosystem Snowflake has a more extensive ecosystem, with integrations for various data sources, BI tools, and machine learning platforms. Firebolt's ecosystem is growing but currently more limited In summary, while both platforms offer scalability and security, Firebolt excels in raw query performance and cost-efficiency for high-performance analytics workloads, while Snowflake provides a more mature ecosystem and flexible pricing options for a broader range of use cases.[1][2][4] Sources [1] Snowflake vs Firebolt Whitepaper https://lnkd.in/gtPnfRGR [2] snowflake vs firebolt: Which Tool is Better for Your Next Project? https://lnkd.in/gEvr8iye [3] Snowflake vs Firebolt: Data Warehousing Comparison - 6Sense https://lnkd.in/gz5EPSDM [4] Data Warehouse Tools Comparison: Snowflake vs. Firebolt https://lnkd.in/ggQJ_HYk [5] Firebolt vs Snowflake: Compare data warehousing platforms https://lnkd.in/gW67rkDf

At Coditation, we understand the importance of choosing the right data warehousing solution for your organization. That's why our team of experts has conducted an in-depth analysis of two leading cloud-based data warehousing platforms: Snowflake and Amazon Redshift. In our latest blog post, we dive deep into the performance benchmarks, feature sets, and our hands-on experience working with both Snowflake and Redshift. We compare their query performance using the TPC-H benchmark, showcasing execution times for complex analytical queries on a 1 TB dataset. Key highlights from our analysis include: - Detailed performance metrics, including query response times and storage footprint reduction - Comprehensive feature comparison, covering data integration, query language compatibility, scalability, security, and pricing. - Real-world insights from our experience working with Snowflake and Redshift Recommendations for evaluating and selecting the right platform based on your organization's specific needs Whether you're considering Snowflake for its unique architecture and data sharing capabilities or Redshift for its seamless integration with the AWS ecosystem, our blog post provides valuable insights to guide your decision-making process. 🔗 https://lnkd.in/dkvdpfPf

Introducing @Amazon Web Services (AWS) QuickSight: Fast and powerful business intelligence for your data warehouse. 🤓 Read this article for a first-hand account that illustrates how it helps you harness the power of data. For additional information, contact Pcnaid Inc.

Introducing @Amazon Web Services (AWS) QuickSight: Fast and powerful business intelligence for your data warehouse. 🤓 Read this article for a first-hand account that illustrates how it helps you harness the power of data. For additional information, contact Miller Industrial Inc..

Apache Doris can be a powerful, open source alternative to #Redshift, #Snowflake, and #BigQuery for building a data warehouse. It stands out for its real-time capabilities and provides both on-premise and cloud deployment options. However, due to the mechanical differences, migrating from these tools to Doris is a complicated, systemic project. Just run into this article by Sammeer Dannave from MSys Technologies, who is drawing a mind map about the crucial steps to be attentive to during this process: Streamlining Data Warehouse Migrations From RedShift, Snowflake, and BigQuery to Apache Doris #dataengineering #bigdata #dataplatform https://lnkd.in/ddV7TD6N

A few key points to look into during the comparison: ⭐ Data storage: Apache Doris is a column-oriented data warehouse and provides row/column hybrid storage to speed up point queries. ⭐ Data types: Apache Doris can accommodate complex data types such as Array, Map, Struct, JSON, and provides VARIANT for semi-structured data processing. ⭐ Data modeling: Apache Doris arranges data in three types of data models, shards data into partitions and buckets, and supports flexible partitioning and bucketing methods. ⭐ SQL: Apache Doris supports standard SQL and provides an SQL Dialect Convertor to facilitate migration from various databases. ⭐ Indexing: Apache Doris provides a rich collection of indexes to speedup different query workloads, such as inverted index for full-text searches. ⭐ Materialized View: Apache Doris supports Materialized View for pre-computation and Asynchronous Materialized View to accelerate cross-table joins. ⭐ Semi-structured data analysis: Apache Doris is fast and cost-efficient at analyzing semi-structured data. ⭐ Data lake analytics / data lakehouse: Apache Doris allows direct queries on external data sources including Hive, Hudi, Iceberg, Paimon, Elasticsearch, HDFS, S3, and those using the standard JDBC protocol (MySQL, PostgreSQL, Oracle, SQLServer, Clickhouse, SAP HANA, Trino, OceanBase, and Apache Doris itself). ⭐ Performance: Apache Doris publishes its Star Schema Benchmark, TPC-H Benchmark, TPC-DS Benchmarkresults for users' reference. For more information about Doris or assistance in a PoC or data warehouse migration, the Doris makers are active on Slack and will gladly help you with patience. https://lnkd.in/ghMuVZW2

Introducing @Amazon Web Services (AWS) QuickSight: Fast and powerful business intelligence for your data warehouse. 🤓 Read this article for a first-hand account that illustrates how it helps you harness the power of data. For additional information, contact PRIVAXI.

Introducing @Amazon Web Services (AWS) QuickSight: Fast and powerful business intelligence for your data warehouse. 🤓 Read this article for a first-hand account that illustrates how it helps you harness the power of data. For additional information, contact JuvinTech.

Launching a new blog series on Snowflake, cloud-based data platform. Introduction to Snowflake: Powering the AI Data Cloud Revolution In this introductory article, I cover: - What Snowflake is and why it matters - Snowflake's unique architecture - Key features that set it apart from traditional data warehouses Link: https://lnkd.in/gv3XhkvR Stay tuned for upcoming posts where I'll dive deeper into specific Snowflake features, best practices, and real-world use cases. For those interested in a more technical deep-dive, I've attached the original Snowflake research paper at the end of the blog post. While it's a few years old now, it provides valuable insights into the foundational architecture and principles of Snowflake. Have you used Snowflake in your data projects? I'd love to hear about your experiences in the comments! #Snowflake #DataWarehouse #CloudComputing #BigData #DataEngineering