Data quality is non-negotiable
In today's fast-paced, data-driven world, deeper data insights and faster time to value are paramount if you want your business to stay competitive and thrive.
Decision-makers need instant access to all their data sources to make sound business decisions — and they need to have trust in their data. However, data quality is often overlooked. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. What’s going on?
3 common data complexity obstacles
Obstacle 1: Too much data, too little time
The proliferation of data sources and exponential data volume growth have inundated organizations with vast amounts of information. Extracting valuable insights from this sea of data within a limited timeframe is a significant challenge. Businesses often struggle to keep up with the pace of data generation and the demands for timely analysis. The need for accuracy and quality in data processing further exacerbates this challenge.
If you have more data than you can manage, you need a data management solution with pervasive data quality and solid connectivity options to many data sources.
Obstacle 2: Suboptimal performance or compromised data quality
Data quality is a continuous concern that requires consistent focus and collaboration between IT and data teams. Achieving and maintaining high-quality data is not a one-time project — it’s an ongoing effort that demands continuous monitoring, profiling, and improvement. This iterative process requires extensive compute resources to efficiently handle the complexities of the data processing. Many organizations struggle to strike the right balance between allocating sufficient computation power for data processing tasks and maintaining high data quality.
To solve this problem, you need a modern data management platform that can scale across any deployment footprint — on-premises, cloud, and hybrid cloud — and seamlessly integrate with modern cloud data warehouse solutions to accelerate data processing. The solution should provide data observability for continuous data quality monitoring and self-service capabilities, allowing data teams to easily spot and address data quality issues on demand.
Obstacle 3: Achieving data quality without compromising security
Data residency requires that certain data should be stored and processed within specific geographic boundaries or jurisdictions. This is often driven by regulatory compliance requirements, privacy concerns, or organizational policies.
A modern data management solution meets security and compliance needs by analyzing the data within the existing data residency boundaries and providing data quality insights without moving the data.
3 reasons to pair your Snowflake investment with Talend
With the powerful capabilities of Talend, a Qlik company, you can unlock the full potential of your Snowflake investment. You'll be able to tackle complex data challenges, enhance data quality and governance, and drive actionable business insights.
Reason 1. Broad connectivity and rapid migration
Talend offers purpose-built data connectors for Snowflake, which means we provide pre-configured components and connectors specifically designed to facilitate data ingestion from different sources into Snowflake. These connectors enable seamless connectivity between Talend and Snowflake, so you can easily extract data from a variety of sources such as databases, files, cloud applications, APIs, and more.
Whether your data resides in on-premises systems, cloud storage, or other data repositories, Talend's connectors provide the necessary functionality to quickly extract that data and centralize it in your Snowflake Data Cloud. One of the key advantages of using purpose-built Talend connectors for Snowflake is their ability to handle data volumes of any size, complementing Snowflake’s ability to store, process, and scale massive amounts of data.
Stitch, a no-code ETL from Talend, offers the easiest way to load data into Snowflake for analysis. Select your data sources and desired replication frequency, then let Stitch handle the rest.
We designed the integration between Stitch and Snowflake to be highly efficient and scalable. Use Stitch to optimize your data ingestion process and ensure fast and reliable data transfers — even when dealing with large data volumes. Stitch utilizes Snowflake's scalability and performance features to load data in parallel, maximizing throughput and minimizing latency.
One of the key benefits of using Stitch for data ingestion is its ability to automate the process for citizen integrators, with no coding required. Once the initial setup is complete, Stitch runs on autopilot, ensuring that the data in Snowflake remains up to date with the source systems. This set-it-and-forget-it pipeline automation saves time and effort in getting data to an analysis-ready state.
Reason 2. Continuous data quality checks without performance loss
Talend Data Inventory empowers business users to continuously observe data quality and derive insights, leveraging pushdown of data quality rules and the Talend Trust Score™ computed in Snowflake for high performance. Pushdown refers to the execution of data processing operations within your Snowflake virtual warehouse.
The Talend Trust Score™ provides an overall assessment of data quality, driven by business-context-aware data quality rules. You can leverage the power of Snowflake's computing capabilities to perform the Trust Score™ calculation right in Snowflake across large volumes of data.
Instead of relying on sampling techniques, where only a subset of the data is assessed for quality, Talend's Data Inventory powered by Snowflake computes the Trust Score™ by performing data quality checks on the entire dataset. When you scan your entire dataset, you can have more confidence in the accuracy and reliability of quality assessments across the organization.
Reason 3. Enhanced security and privacy for sensitive data processing at lower cost
Talend's ability to process data natively within Snowflake eliminates the need to transfer the entire dataset from Snowflake to Talend. By eliminating data movement outside the Snowflake environment, you not only reduce cloud ingress and egress costs, but there are also fewer security and residency violation risks.
Achieve high-quality data and governance without trade-offs
Organizations tackling data quality and governance concerns frequently face trade-offs that force tough choices. That could involve choosing between broad connectivity versus fast data integration, system performance versus data quality, and sensitive data processing versus costs.
With Talend, you can unlock the full potential of your Snowflake investment. The combination of Talend and Snowflake offers a powerful solution to drive data quality and governance initiatives, so you can reach your business objectives quickly. You’ll gain the ability to tackle these complex data challenges, raise the bar for data quality and governance, and drive actionable insights — all without compromise.
The powerful synergy between Talend and Snowflake empowers customers to get real-time access to trusted data to achieve greater business outcomes and generate actionable business insights. Talend's trusted data management platform enables customers to drive meaningful outcomes with its comprehensive data quality and governance capabilities. Being a Snowflake Elite Partner, Talend continues to set a high bar for both product integrations and driving joint customer success.— Anoop Sunke, Head of Partner Sales Engineering, Snowflake
Ready to unlock the full potential of your data and take your data quality efforts to the next level? Try the new Talend Trial Guided Experience to sample everything Talend offers your organization.
If you're headed to Snowflake Summit 2023, you can meet the modern data management experts from Talend and Qlik in person. Visit Booth #1631 to learn how our end-to-end enterprise solution for Snowflake can help you overcome your toughest data challenges.