Simplifying Complex Data Workloads in the Cloud

Written by: Praveen Kumar Ramalingam

Simplify intricate data tasks using Snowflake's cloud-native platform, enabling seamless scalability, high concurrency, and optimized performance for complex workloads.


BlueCloud proudly stands at the forefront of data and analytics, GenAI, and cloud transformation. As a leading Snowflake partner, we offer our clients tailored solutions using the latest cloud technologies. Our suite of premium services includes digital strategy, data engineering and analytics, digital services, and cloud operations—all offered at competitive rates, thanks to our unique flexible delivery model.

At BlueCloud, we provide a dedicated Innovation team for strategic and execution support to implement effective business strategies and technology solutions for our diverse clientele, which spans Fortune 500 companies, mid-market firms, and startups. Our commitment is steadfast in fostering customer success within the dynamic GenAI landscape. We prioritize innovation, exceptional customer service, and the cultivation of employee engagement.

Our journey with Snowflake

BlueCloud is an Elite tier partner with Snowflake, enabling them to expedite joint customer digital transformations by harnessing Snowflake's Data Cloud for enhanced performance, flexibility, and scalability. Our expertise in data-driven solutions aligns with Snowflake's vision to empower enterprises through modern cloud data management, unlocking valuable insights and business opportunities for customers.

Challenges with data in today landscape

In today's era, data embodies both immense potential and considerable challenges. The complexity of a workload isn't always tethered to the size of its dataset. Datasets exhibit continuously evolving schemas, a multitude of fields, inconsistencies in data, and highly transactional to name a few.

Concurrently, the substantial surge in data volumes from sources like web traffic, IoT, IoST, etc., along with velocity impacting the time taken for data loading. In this blog, we aim to delve into various solutions offered by cloud data platforms such as Snowflake to overcome these challenges.

Reducing data loading time from hours to minutes

Few years ago, an ETL based approach to data integration might have been a good idea. But, with data volumes increasing exponentially over the last decade, traditional methods are prone to Increased error rates, Lag times, High maintenance, and Lower quality of service.

Data access via Snowflake Marketplace or private share enables organizations to leverage new datasets, including market, identity, geospatial data, etc instantaneously. Zero copy data sharing enables teams to access and analyse internal and external vendor data with minimal to no ETL, no storage fee and always up to date. Listings also let you share data with people in any Snowflake region, across clouds, without performing manual replication tasks. This vastly improves the data freshness, eliminates data silos, and lowers the overall Total Cost of Ownership (TCO). Additionally, various Snowflake connectors such as Salesforce, Kafka connectors etc. enable direct integration, simplifying the data ingestion into Snowflake.

Data pipeline advancements

Data Pipeline Design heavily relies on the database, processing engine, and team expertise, reflecting the chosen architecture. For instance, Hadoop to Spark necessitates modifications or recreation of data pipelines, emphasizing the importance of adaptable architectures.

Snowflake’s continuous data pipeline is a data processing architecture that allows enterprises to process massive amounts of data quickly and evolve as per organizational needs. Snowpark Framework enables Python, Java, and Scala with familiar concepts like DataFrames for data processing in Snowflake. Speeds up pipeline execution for data professionals using their preferred languages. Dynamic Tables for Transformation offers a code-free way to create complex data pipelines in Snowflake.

Snowflake's native execution efficiently processes queries by leveraging its unique architecture, while supporting stored procedures in SQL, JavaScript, and Python for encapsulating complex data transformations, including the transformation part of ELT processes.

Data Handling challenges

Traditional data integration tools excel in handling highly structured and batch data but struggle with newer data types. A more natural way to ingest new data is to pull it in continuously, as data is born with constantly evolving schemas of semi-structured data (e.g., JSON, Avro, ORC, and Parquet), as well as unstructured data in file formats.

Snowflake's SQL access to semi-structured data enables schema-less data pipelines, maintaining original data structure while optimizing storage for high-performance analytics, thus eliminating the need for separate transformations, and reducing costs by avoiding data duplication and management overhead. Snowflake utilizes the VARIANT data type to manage self-describing schemas, enabling the direct loading of semi-structured data into a relational table column. Through Snowflake's Schema-on-Read approach, users can promptly create view definitions over this raw data, facilitating immediate querying capabilities.

Complex Data pipeline development

Complex data pipelines require rigorous testing and latest datasets to validate its production fit, traditional architecture demands significant resources for environment mimics, testing and fallback mechanisms. Modern platforms like Snowflake with its Zero copy clone capabilities makes environment replication instantaneously, thereby enabling better pipeline stability. Snowflake’s time travel capabilities enable quick recovery in case of production misloads and restoration scenarios.

Data Streaming Capabilities

Traditionally, the tools for batch and streaming pipelines have been distinct, and as such, data engineers have had to create and manage parallel infrastructures to leverage the benefits of batch data while still delivering low latency streaming products for real-time use cases.

Snowpipe Streaming, a preview feature, enables swift loading of streaming data rows using the API, reducing latencies and costs compared to traditional methods. Unlike Snowpipe or bulk data loads relying on staged files, this approach writes data directly to Snowflake tables. specifically catering to scenarios where data arrives in rows from business applications, IoT devices, or event sources such as Apache Kafka, including topics coming from managed services such as Confluent Cloud or Amazon MSK. This API streamlines the continuous loading of data into Snowflake without the need for intermediary files.

Integration with Machine Learning Frameworks

Only a handful of organizations have effectively harnessed the potential of their investments in data science and machine learning (ML) to gain a competitive edge, which is truly challenging due to complexities in the data workload and compute requirements.

Snowflake Cortex ML-Based Functions facilitate accelerated analytics with specialized and versatile models available for handling unstructured data with SQL or Python without the need to bring up and manage expensive GPU infrastructure. These models offer capabilities such as Answer Extraction, Sentiment Detection, Text Summarization, and Translation, empowering users to extract information, detect sentiments, summarize content, and perform large-scale translations seamlessly.

Moreover, Snowflake Cortex introduces a range of ML-based models with diverse functionalities. Forthcoming additions, such as Forecasting, Anomaly Detection, classification are to name a few.

Pipeline Stability and monitoring

The traditional ELT execution logs are maintained in different auditing tables to investigate data or data transformation issues. Snowflake Event tables simplify the Exception Handling and Reporting of data pipeline’s failures and logs errors appropriately in event tables for further analysis.

Support for Restarting Failed Jobs: The restart ability of jobs eliminates data gaps and data duplication, which is simplified with snapshot restoration with time travel.

Capturing run time statistics of ELT jobs allows for analysis of all the past ELT runs and gives visibility into future resource needs and performance tuning opportunities., which is done at ease thru query tagging capabilities in snowflake.

Key Takeaways:

Review Data Pipeline Value: Assess existing data pipelines to identify if any solely optimize data layout without adding business value. Simplify processes where possible for improved efficiency.

Align Data Needs and Infrastructure: Compare evolving data needs against current architecture capabilities. Look for opportunities to modernize, simplifying operations without being limited by legacy systems.

Simplify and Eliminate Complexity: Identify and reduce complexity across various data services and silos. Streamline data access, minimize duplication of efforts, and enhance scalability by eliminating unnecessary boundaries of data silos.

Evaluate Cost Efficiency: Analyse the cost structure of core data pipeline services, considering usage-based models and skill requirements. Minimize manual optimization efforts and integrate governance costs into the overall architecture assessment.

Data Monetization: The data within your organization holds significant value. By extending datasets in snowflake data cloud enables more efficient collaboration with others and creates more value thru monetization.

References: Image Credits: Snowflake Documentation