We are thrilled to introduce the Public Preview of our latest integration, now available for SQL Server, Salesforce, and Workday.
Powered by incremental knowledge processing and intelligent optimisations, these ingestion connectors enable seamless, eco-friendly data ingestion from databases and enterprise applications. LakeFlow’s integration with the Knowledge Intelligence Platform enables seamless interaction between serverless compute and Unity Catalog governance, presenting a unified approach to data management and analysis. Ultimately, this suggests that organizations can allocate significantly less time to updating their knowledge and instead focus on deriving greater value from what they already possess?
Broadly speaking, this marks a pivotal milestone in driving progress for knowledge engineering on Databricks through our unified solution for ingestion, transformation, and orchestration, which was first unveiled at the Knowledge + AI Summit. LakeFlow Join integrates smoothly with LakeFlow Pipelines for seamless transformation capabilities, harmoniously aligning with LakeFlow Jobs for efficient orchestration. By aggregating these solutions, clients can effectively deliver more invigorating and superior insights to their organizations.
Challenges in knowledge ingestion
Organisations possess a diverse array of information sources, encompassing enterprise applications, databases, message buses, cloud storage facilities, and additional assets. To manage the complexities of each supply effectively, they often build and maintain tailored intake processes that present a range of difficulties.
- Connecting seamlessly to databases without disrupting the entire supply chain is indeed a challenging task. The constant flux of software APIs makes it arduous for developers to stay abreast of updates, requiring tireless efforts to maintain compatibility and functionality. As a result, the creation and maintenance of customised pipelines necessitate significant investment in terms of effort, optimization, and preservation, potentially leading to decreased efficiency and increased costs.
- Given this level of complexity, developing ingestion pipelines necessitates highly specialized and experienced knowledge engineers with a deep understanding of data workflows and pipeline architecture. Since knowledge shoppers, such as HR analysts and financial planners, heavily rely on specialized engineering teams, this ultimately restricts productivity and innovative potential.
- Without a cohesive architecture, constructing governance, entry management, observability, and lineage across patchworked pipelines proves arduous. This layout permits entry to perilous conditions and regulatory hurdles, further complicating issue resolution efforts.
LakeFlow Join: Streamlined Sustainable Ingestion for Every Staff
By streamlining workflow construction, LakeFlow enables practitioners to effortlessly build scalable knowledge pipelines with ease.
LakeFlow Join is effortlessly configurable and maintains a seamless workflow.
To begin with, connector installation typically requires only a few straightforward steps. When you configure a connector in Databricks, it is fully managed and supported by the platform itself. By implementing this solution, we can significantly reduce the costs associated with maintenance and upkeep. This suggests that access to information doesn’t necessitate specialized expertise – and that understanding can be democratized across your organization.
The Salesforce connector proved straightforward to implement, enabling seamless synchronization of data into our centralized knowledge repository. This has significantly accelerated growth time and reduced ongoing support needs, allowing for an expedited migration.
As the Know-how Lead Software program Engineer at Ruffer, I am delighted to share my expertise and insights with you.
LakeFlow Join is environment friendly
Beneath the surface, LakeFlow’s join pipelines are built upon Delta Lake’s reside tables, engineered to facilitate eco-friendly, incremental data processing with a focus on efficiency and scalability. Mostly, connectors acquire and record data solely on modifications occurring within the supply system. Ultimately, we utilize ‘s domain expertise to fine-tune each connector for maximum efficiency and dependability while minimizing its impact on the overall supply system.
As a result of ingestion, we don’t cease there, instead, our process unfolds to unlock the true potential of the substance within.
By leveraging the power of incremental remodeling, you can effectively create environmentally sustainable materialized views that continuously refine your understanding within the framework of a robust medallion structure. Specifically, Delta reside tables enable incremental updates to your views by only processing changed data, rather than recalculating entire row sets. As time passes, these improvements will substantially amplify the speed and effectiveness of your data transformations, ultimately rendering your entire ETL workflow significantly more streamlined and efficient.
The connector optimizes information exchange by providing a secure and streamlined interface that effortlessly integrates Salesforce with Databricks, thereby expanding our data-handling capabilities. The time needed to gather and synthesise knowledge has decreased dramatically, from approximately three hours down to a mere thirty minutes.
The analytics world has seen a significant shift in recent years, with the rise of big data, artificial intelligence, and machine learning. As we move forward, it’s crucial to have skilled professionals who can harness these technologies to drive business growth and stay ahead of the competition.
LakeFlow joins seamlessly with the Knowledge Intelligence Platform, leveraging its robust infrastructure and advanced analytics capabilities.
LakeFlow joins seamlessly with the rest of your Databricks ecosystem. Like the entirety of your expertise and artificial intelligence assets, this is governed by Unity Catalog, leveraging Delta Live Tables on a serverless computing platform, and coordinated through Databricks Workflows. This enables seamless unification of monitoring capabilities across your entire data ingestion pipeline infrastructure. As a direct outcome of sharing the same ecosystem, users can seamlessly leverage Databricks SQL, AI/BI, and Mosaic AI capabilities to extract maximum value from their data.
“With the introduction of Databricks’ LakeFlow Connector for SQL Server, we can seamlessly eliminate the need for intermediary data processing, enabling a more direct connection between our supply database and Databricks.” With integrated CDC capabilities, organizations can expect faster onboarding, reduced costs, and simplified maintenance of external data integration solutions. This characteristic will significantly enhance our workflow by efficiently processing and organizing our information flow.
As I reflect on my journey as a database administrator at CoStar, I am reminded of the significance of my role in ensuring the integrity and efficiency of our organization’s data infrastructure.
An thrilling LakeFlow roadmap
The primary wave of connectors enables the creation of SQL Server, Salesforce, and Workday pipelines through API integration. But the real excitement lies ahead, as we continue to refine and expand this innovative platform. Within the next few months, we intend to initiate a series of Personal Previews showcasing our connectors to external knowledge resources.
- ServiceNow
- Google Analytics 4
- SharePoint
- PostgreSQL
- SQL Server on-premises
The roadmap also includes a more detailed profile for each connector. This will likely embody:
- UI for connector creation
- Knowledge lineage
- SCD sort 2
- Strong schema evolution
- Knowledge sampling
Broadly speaking, LakeFlow Connect is the core component of LakeFlow. By the end of the year, we anticipate unveiling LakeFlow Pipelines for streamlined transformation and LakeFlow Jobs for enhanced orchestration – a significant milestone in the development of our respective platforms. Once established in their new environment, these species will no longer necessitate any further relocation efforts. By implementing Delta Live Tables and Workflows now, you can efficiently accommodate these latest enhancements.
Getting began with LakeFlow Join
The solution facilitates seamless ingestion from both Azure SQL Database and AWS RDS for SQL Server, leveraging incremental reads powered by Change Data Capture (CDC) and real-time monitoring capabilities. What are the key factors that determine success in today’s fast-paced digital landscape?
Incorporating data from Salesforce Sales Cloud enables seamless integration of CRM insights with Knowledge Intelligence Platform, fostering deeper understanding and more comprehensive analysis. What specific aspects of teaching would you like to learn more about? Would you like to explore strategies for engaging students, creating effective lesson plans, or developing your own teaching style? Perhaps you’re interested in learning about innovative technologies or tools that can enhance the learning experience. Let me know and I’ll be happy to share some insights!
By integrating with Workday’s Reviews-as-a-Service (RaaS), you can seamlessly ingest feedback and utilize it to inform data-driven decisions, enhancing review analysis and enrichment capabilities. Learning opportunities abound when exploring the subject further.
“The Salesforce connector provided by LakeFlow Join has been a game-changer for us, allowing seamless access to our Salesforce data and eliminating the need for a costly third-party integration solution.”
— Amine Hadj-Youcef, Answer Architect, ENGIE
To gain access to the preview, please reach out to your dedicated Databricks account representative.
LakeFlow joins leverage serverless compute capabilities for processing Delta Live Tables. Due to this fact:
- Serverless computing should be enabled in your account; consult the instructions on taking action for both and, as well as the comprehensive list of serverless-enabled regions for and.
- To successfully utilize your workspace in Unity Catalog, ensure that this feature is properly configured and accessible.
Please confirm the missing context for completion.
