%PDF-1.4 %âãÏÓ 1 0 obj << /Type /Catalog /Pages 2 0 R >> endobj 2 0 obj << /Type /Pages /Count 5 /Kids [5 0 R 7 0 R 9 0 R 11 0 R 13 0 R] >> endobj 3 0 obj << /Type /Font /Subtype /Type1 /BaseFont /Helvetica >> endobj 4 0 obj << /Type /Font /Subtype /Type1 /BaseFont /Helvetica-Bold >> endobj 5 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 6 0 R >> endobj 6 0 obj << /Length 5171 >> stream BT /F2 22 Tf 0.06 0.08 0.12 rg 1 0 0 1 46 789.89 Tm (8 Best Streaming ETL Software Tools for) Tj ET BT /F2 22 Tf 0.06 0.08 0.12 rg 1 0 0 1 46 762.89 Tm (Snowflake Pipelines) Tj ET BT /F2 11 Tf 0.72 0.14 0.18 rg 1 0 0 1 46 725.89 Tm (TechRounder PDF Edition) Tj ET BT /F1 9.5 Tf 0.36 0.39 0.46 rg 1 0 0 1 46 709.89 Tm (Live article: https://www.techrounder.com/software/best-streaming-etl-software-tools-for-snowflake-pipelines/) Tj ET q 0.82 0.85 0.9 RG 1 w 46 691.39 m 549.28 691.39 l S Q BT /F1 10 Tf 0.24 0.27 0.32 rg 1 0 0 1 46 679.39 Tm (By Vipin PG | Published September 10, 2026 | Updated September 10, 2026 | Format: Article | 8 min read) Tj ET BT /F2 13 Tf 0.72 0.14 0.18 rg 1 0 0 1 46 656.39 Tm (In brief) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 636.39 Tm (Snowflake can process fresh data quickly, but the data reaching it may already be delayed. That delay) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 621.39 Tm (usually begins upstream.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 596.39 Tm (Snowflake can process fresh data quickly, but the data reaching it may already be delayed. That delay) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 581.39 Tm (usually begins upstream.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 559.39 Tm (Production databases continue receiving orders, account changes, transactions, inventory updates,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 544.39 Tm (application events, and customer activity between scheduled ETL runs. Even pipelines described as) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 529.39 Tm ("near real time" can introduce meaningful delays when extraction, staging, merging, and) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 514.39 Tm (transformation run on separate schedules.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 492.39 Tm (Streaming ETL software reduces that delay by continuously moving new and changed data toward) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 477.39 Tm (Snowflake rather than rebuilding datasets in periodic batches.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 449.39 Tm (8 Streaming ETL Software Tools for Snowflake Pipelines) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 419.39 Tm (1. Artie) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 397.39 Tm (Artie is well suited to Snowflake teams that need to move changing operational database data into the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 382.39 Tm (warehouse continuously with low latency and limited infrastructure overhead.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 360.39 Tm (Artie is a fully managed CDC streaming platform. Teams connect a source database, choose the tables) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 345.39 Tm (they want replicated, and configure Snowflake as the destination. Artie performs the historical) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 330.39 Tm (backfill before switching to continuous CDC, keeping subsequent inserts, updates, and deletes aligned) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 315.39 Tm (with the operational source.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 293.39 Tm (For PostgreSQL, Artie reads changes from the WAL through logical replication. It can maintain) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 278.39 Tm (source-aligned tables in Snowflake using merge operations. Append-oriented history workloads can) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 263.39 Tm (use Snowpipe Streaming. The product also includes mechanisms for delivery reliability and recovery.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 241.39 Tm (Artie currently provides exactly-once delivery, automatic schema evolution, initial and online backfills,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 226.39 Tm (pipeline observability, SCD Type 1 replication, and a history mode that preserves row versions over) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 211.39 Tm (time. History tables can record each create, update, and delete along with processing and source) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 196.39 Tm (timestamps.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 174.39 Tm (Common schema changes, including new destination tables and columns, can be handled automatically.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 159.39 Tm (Artie can optionally remove deleted source columns after a verification period. Column inclusion and) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 144.39 Tm (exclusion controls also let teams keep unnecessary or sensitive fields out of Snowflake.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 116.39 Tm (2. Estuary Flow) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 94.39 Tm (Estuary Flow combines CDC, streaming data movement, batch ingestion, and transformations in a) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 79.39 Tm (streaming-first platform.) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 1 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/best-streaming-etl-software-tools-for-snowflake-pipelines.pdf) Tj ET endstream endobj 7 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 8 0 R >> endobj 8 0 obj << /Length 5393 >> stream BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 789.89 Tm (For Snowflake pipelines, Estuary can capture changes from sources such as PostgreSQL and) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 774.89 Tm (materialize those collections into Snowflake. Its Snowflake integration supports standard and delta) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 759.89 Tm (update patterns, with Snowpipe Streaming available for lower-latency ingestion.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 737.89 Tm (Captured data is stored as a reusable collection that can later be materialized into one or more) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 722.89 Tm (destinations. This gives Estuary a different architecture from a conventional point-to-point replication) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 707.89 Tm (pipeline.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 679.89 Tm (3. Striim) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 657.89 Tm (Striim has a long-standing focus on enterprise data streaming, CDC, and real-time integration. Its) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 642.89 Tm (Snowflake offering can continuously move changes from databases such as Oracle, SQL Server,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 627.89 Tm (PostgreSQL, MySQL, and MariaDB into Snowflake after an initial synchronization.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 605.89 Tm (Striim also supports in-flight processing. Data can be filtered, enriched, masked, denormalized, or) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 590.89 Tm (otherwise transformed before reaching Snowflake. Striim describes this as real-time ETL, with) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 575.89 Tm (SQL-oriented processing logic applied to streaming data before the warehouse layer when needed.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 547.89 Tm (4. Matillion) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 525.89 Tm (Matillion combines data ingestion and transformation in a platform closely associated with cloud data) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 510.89 Tm (warehouses.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 488.89 Tm (For Snowflake, Matillion CDC can continuously capture changes from supported operational databases) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 473.89 Tm (and load them into Snowflake. Its current documentation includes CDC sources such as Oracle,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 458.89 Tm (PostgreSQL, and SQL Server, along with target representations such as copied tables, soft-delete) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 443.89 Tm (patterns, and change logs.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 421.89 Tm (Matillion is useful when ingestion and warehouse transformation are both significant parts of the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 406.89 Tm (pipeline. Teams can bring CDC records into Snowflake and use Matillion's transformation capabilities) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 391.89 Tm (to build analytics-ready models.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 363.89 Tm (5. Qlik Replicate) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 341.89 Tm (Qlik Replicate focuses on log-based CDC and enterprise-scale data replication. It can capture changes) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 326.89 Tm (from operational databases and continuously apply them to targets such as Snowflake. Qlik provides a) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 311.89 Tm (Snowflake-optimized warehouse loading mode designed for the way massively parallel analytical) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 296.89 Tm (systems ingest and merge data.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 274.89 Tm (Source coverage is one of the platform's notable characteristics. Large enterprises may need to) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 259.89 Tm (move data into Snowflake from databases, enterprise applications, mainframe-related systems, cloud) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 244.89 Tm (platforms, and streaming infrastructure. Qlik's replication stack supports this wider range of source) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 229.89 Tm (environments.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 201.89 Tm (6. Hevo) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 179.89 Tm (Hevo provides a managed, no-code pipeline environment for database, SaaS, file, and other source) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 164.89 Tm (types.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 142.89 Tm (Its pipeline architecture continuously ingests source data, applies configured transformations and) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 127.89 Tm (schema mappings, and loads records into destinations including Snowflake. The platform supports) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 112.89 Tm (near-real-time transfer, automatic schema management, live monitoring, and built-in or Python-based) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 97.89 Tm (transformations.) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 2 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/best-streaming-etl-software-tools-for-snowflake-pipelines.pdf) Tj ET endstream endobj 9 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 10 0 R >> endobj 10 0 obj << /Length 5778 >> stream BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 789.89 Tm (For database replication, Hevo can use source-specific CDC methods. Its MySQL integration, for) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 774.89 Tm (example, uses binlog-based replication for low-latency data movement into Snowflake. SQL Server) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 759.89 Tm (pipelines can use Change Tracking. PostgreSQL workflows can use incremental or CDC-based) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 744.89 Tm (synchronization depending on the configuration.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 716.89 Tm (7. Fivetran) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 694.89 Tm (Fivetran provides managed data movement across a broad connector ecosystem and supports) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 679.89 Tm (Snowflake as both a destination and a source. For Snowflake destinations, Fivetran continuously) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 664.89 Tm (synchronizes source schemas and incrementally loads changes into the warehouse. The platform uses) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 649.89 Tm (Snowflake staging and SQL merge operations to maintain destination tables.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 627.89 Tm (Fivetran also provides high-volume database connectors that use log-based CDC for systems such as) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 612.89 Tm (Oracle and SQL Server. Reading source logs avoids repeated scans of transactional tables and can) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 597.89 Tm (support high-volume replication with less impact on source systems.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 569.89 Tm (8. Informatica) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 547.89 Tm (Informatica supports streaming Snowflake pipelines as part of a broader enterprise data) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 532.89 Tm (management platform. Its cloud data ingestion and replication capabilities support CDC from) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 517.89 Tm (transactional systems into cloud analytical platforms such as Snowflake. Informatica describes) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 502.89 Tm (continuous CDC pipelines for sources including Oracle, SQL Server, SAP-related environments,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 487.89 Tm (Salesforce, and other enterprise systems.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 465.89 Tm (The platform is particularly relevant when Snowflake is part of a larger enterprise modernization) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 450.89 Tm (program. Informatica combines ingestion with data quality, governance, lineage, cataloging,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 435.89 Tm (transformation, and other data management capabilities. Its support for older enterprise environments) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 420.89 Tm (also makes it relevant to organizations working across legacy and cloud systems.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 392.89 Tm (Where Streaming Snowflake Pipelines Fail) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 368.89 Tm (Getting the first set of rows into Snowflake is usually the easy part.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 346.89 Tm (Operational problems appear later. A source schema changes, traffic spikes, Snowflake slows down,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 331.89 Tm (a historical table needs rebuilding, or the team discovers that the source and destination have drifted) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 316.89 Tm (apart.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 294.89 Tm (These failure modes provide a more useful way to evaluate pipeline architecture than connector) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 279.89 Tm (checklists alone.) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 257.89 Tm (Failure Mode | What Happens | What the Pipeline Needs) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 240.89 Tm (Schema drift | Source adds, alters, or removes fields | Schema detection and safe destination evolution) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 223.89 Tm (Destination slowdown | Snowflake cannot consume changes at the normal rate | Buffering and backpressure) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 210.89 Tm (handling) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 193.89 Tm (Pipeline restart | Workers fail or deployment restarts | Durable checkpoints and deterministic recovery) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 176.89 Tm (Duplicate delivery | Events are replayed after failure | Idempotent or exactly-once processing) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 159.89 Tm (Large initial load | Historical data must be copied while production continues changing | Concurrent backfill and) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 146.89 Tm (CDC) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 129.89 Tm (Missing deletes | Destination retains records removed upstream | Delete-aware CDC) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 112.89 Tm (Source traffic spike | Transaction volume rises rapidly | Scalable capture and buffering) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 95.89 Tm (Transformation lag | Raw tables are fresh but business models are stale | Continuous or sufficiently frequent) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 82.89 Tm (transformation) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 3 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/best-streaming-etl-software-tools-for-snowflake-pipelines.pdf) Tj ET endstream endobj 11 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 12 0 R >> endobj 12 0 obj << /Length 4756 >> stream BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 789.89 Tm (Bad source deployment | A new column or type breaks downstream assumptions | Schema monitoring and) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 776.89 Tm (controlled propagation) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 759.89 Tm (Full table rebuild | A table must be refreshed without interrupting downstream users | Online or staged backfill) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 746.89 Tm (strategy) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 729.89 Tm (These operational characteristics can matter more than connector count. A pipeline may support) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 714.89 Tm (PostgreSQL and Snowflake on paper yet still struggle with a high-volume production workload if it) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 699.89 Tm (cannot safely manage backfills, replication slot pressure, destination outages, or schema evolution.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 671.89 Tm (How Streaming Design Affects Snowflake Cost) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 647.89 Tm (Increasing pipeline frequency can also change Snowflake economics. Applying every individual row) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 632.89 Tm (change to Snowflake immediately may create inefficient destination activity.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 610.89 Tm (Production pipelines commonly buffer changes and write them efficiently while maintaining the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 595.89 Tm (required freshness SLA. The appropriate latency target depends on the workload and the cost of) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 580.89 Tm (maintaining it.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 558.89 Tm (Teams need to balance:) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 536.89 Tm (- Data freshness) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 520.09 Tm (- Destination compute) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 503.29 Tm (- Write frequency) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 486.49 Tm (- Merge efficiency) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 469.69 Tm (- Pipeline reliability) Tj ET BT /F1 10.5 Tf 0.2 0.23 0.28 rg 1 0 0 1 46 452.89 Tm (- Data volume) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 436.09 Tm (A fraud-monitoring workflow may justify aggressive freshness requirements. A finance model used) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 421.09 Tm (three times per day may tolerate a longer interval. For many Snowflake workloads, sub-minute) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 406.09 Tm (freshness can provide more practical value than pushing every pipeline toward millisecond latency.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 378.09 Tm (Why Artie Fits Snowflake CDC Pipelines) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 354.09 Tm (All eight platforms can help keep Snowflake data current, but they solve different surrounding) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 339.09 Tm (problems. An enterprise integration suite may suit a company that also needs mainframe) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 324.09 Tm (modernization, broad application integration, data governance, complex transformations, and CDC) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 309.09 Tm (within one vendor ecosystem.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 287.09 Tm (A connector platform can make sense when the data stack depends heavily on hundreds of SaaS) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 272.09 Tm (applications. Streaming processing systems are useful when substantial enrichment must happen) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 257.09 Tm (before data lands in Snowflake.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 235.09 Tm (Artie is more narrowly aligned with teams whose requirement is:) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 213.09 Tm (Keep production database data continuously synchronized with Snowflake without requiring the data) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 198.09 Tm (team to operate a homegrown CDC stack.) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 176.09 Tm (That narrower focus is the main reason Artie takes the first position in this list.) Tj ET BT /F2 15 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 148.09 Tm (FAQs) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 118.09 Tm (What is streaming ETL for Snowflake?) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 4 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/best-streaming-etl-software-tools-for-snowflake-pipelines.pdf) Tj ET endstream endobj 13 0 obj << /Type /Page /Parent 2 0 R /MediaBox [0 0 595.28 841.89] /Resources << /Font << /F1 3 0 R /F2 4 0 R >> >> /Contents 14 0 R >> endobj 14 0 obj << /Length 5504 >> stream BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 789.89 Tm (Streaming ETL continuously captures new or changed data, processes it, and delivers it into) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 774.89 Tm (Snowflake as those changes occur. Database pipelines commonly use change data capture to read) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 759.89 Tm (transaction logs and detect inserts, updates, and deletes. Transformations may happen during the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 744.89 Tm (stream or after the data reaches Snowflake, depending on the architecture.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 716.89 Tm (Is CDC the same as streaming ETL?) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 694.89 Tm (No. CDC is a technique for detecting database changes. Streaming ETL covers the wider pipeline that) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 679.89 Tm (captures, transports, optionally transforms, and loads continuously changing data. CDC is often the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 664.89 Tm (extraction mechanism within a streaming ETL or ELT architecture, particularly when PostgreSQL,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 649.89 Tm (MySQL, Oracle, or SQL Server is the source.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 621.89 Tm (Should transformations happen before or after data reaches Snowflake?) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 599.89 Tm (It depends on the workload. Security filtering, masking, and certain operational transformations may) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 584.89 Tm (need to happen before loading. Analytics-oriented transformations are often easier to manage after) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 569.89 Tm (ingestion using Snowflake SQL, dbt, Dynamic Tables, or similar tools. Separating replication from) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 554.89 Tm (analytical transformation can also simplify pipeline ownership and debugging.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 526.89 Tm (How fresh does Snowflake data need to be?) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 504.89 Tm (The appropriate freshness target depends on the business decision using the data. Fraud detection,) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 489.89 Tm (operational dashboards, AI agents, personalization, and customer-facing analytics may need updates) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 474.89 Tm (within seconds or minutes. Traditional executive reporting may tolerate longer intervals. Teams should) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 459.89 Tm (define freshness as an end-to-end SLA based on the workload and its actual latency requirements.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 431.89 Tm (Why use log-based CDC instead of frequent database queries?) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 409.89 Tm (Log-based CDC reads changes already recorded in the database's transaction log. This reduces the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 394.89 Tm (need for repeated table scans, can lower load on production databases, and makes continuous capture) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 379.89 Tm (of inserts, updates, and deletes easier. Transaction logs also provide an ordered record of changes) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 364.89 Tm (for downstream replication systems.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 336.89 Tm (What happens when a Snowflake streaming pipeline falls behind?) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 314.89 Tm (A well-designed pipeline buffers changes and continues tracking source progress while the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 299.89 Tm (destination recovers. Once Snowflake can accept changes normally again, the pipeline can process the) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 284.89 Tm (backlog. Teams should evaluate checkpointing, backpressure handling, recovery behavior, source-log) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 269.89 Tm (retention, observability, and delivery semantics before using a pipeline for critical real-time) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 254.89 Tm (workloads.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 226.89 Tm (How does schema evolution affect streaming pipelines?) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 204.89 Tm (Schema evolution occurs when developers change the structure of a source table by adding or) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 189.89 Tm (removing columns, changing field types, or making similar modifications. A streaming pipeline needs) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 174.89 Tm (to detect those changes and determine how to represent them safely in Snowflake. Weak) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 159.89 Tm (schema-evolution handling can cause ordinary application deployments to break replication or leave) Tj ET BT /F1 11 Tf 0.14 0.16 0.2 rg 1 0 0 1 46 144.89 Tm (downstream data incomplete.) Tj ET BT /F2 13 Tf 0.08 0.1 0.14 rg 1 0 0 1 46 116.89 Tm (References) Tj ET BT /F1 10 Tf 0.18 0.2 0.24 rg 1 0 0 1 46 96.89 Tm (1. artie.com - https://www.artie.com/) Tj ET q 0.86 0.88 0.92 RG 1 w 46 42 m 549.28 42 l S Q BT /F1 8.4 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 30 Tm (TechRounder | Page 5 of 5) Tj ET BT /F1 7.2 Tf 0.42 0.45 0.5 rg 1 0 0 1 46 19 Tm (https://www.techrounder.com/pdf/blog/best-streaming-etl-software-tools-for-snowflake-pipelines.pdf) Tj ET endstream endobj xref 0 15 0000000000 65535 f 0000000015 00000 n 0000000064 00000 n 0000000147 00000 n 0000000217 00000 n 0000000292 00000 n 0000000434 00000 n 0000005656 00000 n 0000005798 00000 n 0000011242 00000 n 0000011385 00000 n 0000017215 00000 n 0000017359 00000 n 0000022167 00000 n 0000022311 00000 n trailer << /Size 15 /Root 1 0 R >> startxref 27867 %%EOF