Leveraging Time Series Analytics: The Synergy of Seeq and Databricks
In the realm of data analytics, the combination of purpose-built solutions can unlock unprecedented insights, particularly when handling time series data. Seeq and Databricks, two leading platforms in the industrial analytics and AI space, have forged a powerful integration that harnesses the strength of Seeq’s expertise in time series analytics and connectivity alongside Databricks’ robust data processing and machine learning capabilities. This collaboration has empowered organizations to derive deeper insights, enhance operational efficiency, and fuel innovation.
“We are very excited about this partnership, as it will be mutually beneficial for Databricks, Seeq, and their shared customers. Seeq brings key time-series functionality that just isn’t available in other solutions. Seeq also simplifies the complexities of connecting to various types of process data sources. Databricks brings scalable, elastic data engineering and data science capabilities at an affordable price. Seeq can bring data to Databricks for complementary analytic purposes within Databricks. Databricks can serve cleansed and refined IIoT data to Seeq for self-service analytics. This partnership should make this one-two punch even more powerful!” – Brent Railey, Chief Data & Analytics Officer of Chevron Phillips Chemical Company
The Seeq and Databricks integration is a game-changer for businesses dealing with complex time series data in four key categories.
Specialization in Time Series Data
At Seeq, our strength lies in our specialized approach to handling time series data. This starts with the foundation of the Seeq platform, which serves as the central hub for connecting and aligning a wide range of data sources, including the Databricks Lakehouse platform. This extensive connectivity ensures subject matter experts have on-demand access to the time series data necessary to drive industrial enterprise monitoring workflows through analytics. Once the data is connected, the Seeq platform excels in processing, analyzing, and visualizing the data, enabling users to identify patterns, anomalies, and correlations within complex data sets. With this integration, Seeq not only connects to the data stored in the Databricks Lakehouse, but also enriches the flow of data into the data processing pipeline into Databricks, resulting in reduced time to value.
Unified Time Series Analysis and Machine Learning
The integration of Seeq and Databricks combines time series connectivity, cleansing and contextualization with scalable machine learning, without the burden of connecting to and cleansing the data. This enriches the overall effectiveness of the data processing pipeline. By using Seeq to access Databricks’ powerful Lakehouse platform, which can store and process large volumes of data efficiently, shared customers can perform more sophisticated analytics and machine learning workflows. This synergy empowers organizations to build predictive models that incorporate temporal patterns, leading to more accurate forecasts and actionable insights based on historical and near real-time data that can be quickly operationalized.
Data-Driven Decision-Making at Scale
The partnership also enables organizations to rapidly gain holistic insights into their operational processes. Together, Seeq and Databricks empower businesses to analyze and correlate data from various OT and IT sources, enabling them to identify and mitigate patterns, trends, and anomalies that impact operational efficiency. This holistic approach fosters a deeper understanding of the interconnectedness between operational and informational data, facilitating proactive decision-making to optimize processes, mitigate potential risks, and provide actionable information to all parts of the organization in a contextual manner.
With this actionable information in hand, teams can prioritize issues and make truly data-driven decisions about their operations, ultimately impacting the bottom line of their businesses. Example use cases include, understanding energy storage with respect to market prices or generation prediction, customer 360 analysis inclusive of actual production capacity, or control tower algorithms that include subject matter expert insights to identify potential bottlenecks in the supply chain.
Enhanced Collaboration and Visibility
Finally, this integration fosters enhanced collaboration and visibility across the organization. By offering a unified data environment, OT and IT teams can work more closely to promote a culture of data access and sharing that promotes holistic understanding of data-driven insights. Leveraging the Unity catalog and Delta Sharing, this collaboration can even extend securely across organizational boundaries.
Better Together
The integration of Seeq and Databricks represents a significant advancement in time series analytics, enabling organizations to harness the power of specialized time-based data analysis and machine learning for improved decision-making and operational efficiency. By leveraging the strengths of both platforms, businesses can unlock valuable insights, drive innovation, and gain a competitive edge in their respective industries, all while effectively managing and analyzing complex time series data.
Learn more about the Seeq and Databricks partnership here.
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