When you’re serving customers online, speed is essential for a good experience. As the amount of data in a database grows, queries that used to be fast can slow down.

For example, if a query has to scan every row because a table is missing an index, response times that were acceptable with a thousand rows can turn into multiple seconds of waiting once you have a million rows.

If this query is executed every time a user loads your web page, their browsing experience will slow to a crawl, causing user frustration. Slow queries can also impact automated jobs, causing them to time out before completion.

If there are too many of these slow queries executing at once, the database can even run out of connections, causing all new queries, slow or fast, to fail. 

The popular open-source databases MySQL and Google Cloud Platform‘s fully managed version, Cloud SQL for MySQL, include a feature to log slow queries, letting you find the cause, then optimize for better performance.

However, developers and database administrators typically only access this slow query log reactively, after users have seen the effects and escalated the performance degradation.

With Stackdriver Logging and Monitoring, you can stay ahead of the curve for database performance with automatic alerts when query latency goes over the threshold, and a monitoring dashboard that lets you quickly pinpoint the specific queries causing the slowdown.

To get started, import MySQL’s slow query log into Stackdriver Logging.

Once the logs are in Stackdriver, it’s straightforward to set up logs-based metrics that can both count the number of slow queries over time, which is useful for setting up appropriate alerts, and also provide breakdowns by slow SQL statement, allowing speedy troubleshooting.

What’s more, this approach works equally well for managed databases in Cloud SQL for MySQL and for self-managed MySQL databases hosted on Compute Engine. 

For a step-by-step tutorial to set up slow query monitoring, check out Monitoring slow queries in MySQL with Stackdriver. For more ideas about what else you can accomplish with Stackdriver Logging, check out Design patterns for exporting Stackdriver Logging.

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.

At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.

We also open sourced the code on this Github repository, if you’re interested in learning more about this application.

In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report. 

First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax. 

Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:

  - invoke_document_ai:
    call: googleapis.documentai.v1.projects.locations.processors.batchProcess
    args:
        name: ${"projects/" + project + "/locations/eu/processors/" + processorId}
        location: "eu"
        body:
            inputDocuments:
                gcsPrefix:
                    gcsUriPrefix: ${bucket_input + report_id}
            documentOutputConfig:
                gcsOutputConfig: 
                    gcsUri: ${bucket_output + report_id}
            skipHumanReview: true
    result: document_ai_response

Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.

A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:

  - create_callback:
    call: events.create_callback_endpoint
    args:
        http_callback_method: "POST"
    result: callback_details
...
- await_callback:
    try:
        call: events.await_callback
        args:
            callback: ${callback_details}
            timeout: 3600
        result: callback_request
    except:
        as: e
        steps:
            - update_status_to_error:
              ...

In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.

Going further 

If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.

As the volume of data that people and businesses produce continues to grow exponentially, it goes without saying that data-driven approaches are critical for tech companies and startups across all industries. But our conversations with customers, as well as numerous industry commentaries, reiterate that managing data and extracting value from it remains difficult, especially with scale.

Numerous factors underpin the challenges, including access to and storage of data, inconsistent tools, new and evolving data sources and formats, compliance concerns, and security considerations. To help you identify and solve these challenges, we’ve created a new whitepaper, “The future of data will be unified, flexible, and accessible,” which explores many of the most common reasons our customers tell us they’re choosing Google Cloud to get the most out of their data.

For example, you might need to combine data in legacy systems with new technologies. Does this mean moving all your data to the cloud? Should it be in one cloud or distributed across several? How do you extract real value from all of this data without creating more silos?

You might also be limited to analyzing your data in batch instead of processing it in real-time, adding complexity to your architecture and necessitating expensive maintenance to combat latency. Or you might be struggling with unstructured data, with no scalable way to analyze and manage it. Again, the factors are numerous—but many of them accrue to inadequate access to data, often exacerbated by silos, and insufficient ability to process and understand it.

The modern tech stack should be a streaming stack that scales with your data, provides real-time analytics, incorporates and understands different types of data, and lets you use AI/ML to predictively derive insights and operationalize processes. These requirements mean that to effectively leverage your data assets:

• Data should be unified across your entire company, even across suppliers, partners, and platforms., eliminating organizational and technology silos.
• Unstructured data should be unlocked and leveraged in your analytics strategy.
• The technology stack should be unified and flexible enough to support use cases ranging from analysis of offline data to real-time streaming and application of ML without maintaining multiple bespoke tech stacks.
• The technology stack should be accessible on-demand, with support for different platforms, programming languages, tools, and open standards compatible with your employees’ existing skill sets.

With these requirements met, you’ll be equipped to maximize your data, whether that means discerning and adapting to changing customer expectations or understanding and optimizing how your data engineers and data scientists spend their time. In coming weeks, we’ll explore aspects of the whitepaper in additional blog posts—but if you’re ready to dive in now, and to steer your tech company or startup towards success by making your data better work for you, click here to download your copy, free of charge.

How will your organization manage this year’s data growth and business requirements? Your actions and strategies involving data and AI will improve or undermine your organization’s competitiveness in the months and years to come. Our teams at Google Cloud have an eye on the future as we evolve our strategies to protect technology choice, simplify data integration, increase AI adoption, deliver needed information on demand, and meet security requirements. 

Google Cloud worked with* IDC on multiple studies involving global organizations across industries in order to explore how data leaders are successfully addressing key data and AI challenges. We compiled the results in our 2023 Data and AI Trends report. In it, you’ll find the metrics-rich research behind the top five data and AI trends, along with tips and customer examples for incorporating them into your plans. 

1: Show data silos the door

Given the increasing volumes of data we’re all managing, it’s no surprise that siloed transactional databases and warehousing strategies can’t meet modern demands. Organizations want to improve how they store, manage, analyze, and govern all their data, while reducing costs. They also want to eliminate conflicting insights from replicated data and empower everyone with fresh data.

A unified data cloud enables the integration of data and insights into transformative digital experiences and better decision making.

Andi Gutmans, GM and VP of Engineering for Databases, Google Cloud

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In the report, you can learn how to adopt a unified data cloud that supports every stage of the data lifecycle so that you can improve data usage, accessibility, and governance. Inform your strategy by drawing on organizations’ examples such as a data fabric that improves customer experiences by connecting more than 80 data silos, as well as other unified data clouds that save money and simplify growth. 

2: Usher in the age of the open data ecosystem

Data is the key to unlocking AI, speeding up development cycles, and increasing ROI. To protect against data and technology lock-in, more organizations are adopting open source software and open APIs.

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Understand how you can simplify data integration, facilitate multicloud analytics, and use the technologies you want with an open data ecosystem, as described in the report. Learn from metrics about global open source adoption and public dataset usage. And explore how global companies adopted open data ecosystems to improve patient outcomes, increase website traffic by 25%, and cut operating costs by 90%.

3: Embrace the AI tipping point

Pulling useful information out of data is easier with AI and ML. Not only can you identify patterns and answer questions faster but the technologies also make it easier to solve problems at scale.

We’ve reached the AI tipping point. Whether people realize it or not, we’re already using applications powered by AI—every day. Social media platforms, voice assistants, and driving services are easy examples.

June Yang, VP, Cloud AI and Industry Solutions, Google Cloud

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Organizations share how they’re reaching their goals using AI and ML by empowering “citizen data scientists” and having them focus on small wins first. Gain tips from Yang and other experts for developing your AI strategy. And read how organizations achieve outcomes such as a reduction of 7,400 tons per year in carbon emissions and a more than 200% increase in ROI from ad spend by using pattern recognition and other AI capabilities. 

4: Infuse insights everywhere

Yesterday’s BI solutions have led to outdated insights and user fatigue with the status quo, based on generic metrics and old information. Research shows that as new tools come online, expectations for BI are changing, with companies revising their strategies to improve decision making, speed up the development of new revenue streams, and increase customer acquisition and retention by providing individuals with needed information on demand.

Organizations are equipping business decision-makers with the tools they need to incorporate required insights into their everyday workflows.

Kate Wright, Senior Director, Product Management, Google Cloud

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In the report, you’ll discover why and how data leaders are rethinking their BI analytics strategies and applications to improve users’ trust and use of data in automated workflows, customizable dashboards, and on-demand reports. Global companies also share how they improve decision making with self-service BI, customer experiences with IoT analysis, and threat mitigation with embedded analytics.

5: Get to know your unknown data

Increasing data volumes can make it harder to know where and what data they store, which may create risk. Case in point: If a customer unexpectedly shares personally identifiable information during a recorded customer support call or chat session, that data might require specialized governance, which the standardized storage process may not provide.

If you don’t know what data you have, you cannot know that it’s accurately secured. You also don’t know what security risks you are incurring, or what security measures you need to take.

Anton Chuvakin, Senior Staff Security Consultant, Google Cloud

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Check out the report to learn about data security risks that are often overlooked and how to develop proactive governance strategies for your sensitive data. You can also read how global organizations have increased customer trust and productivity by improving how they discover, classify, and manage their structured and unstructured data.  

Be ready for what’s next

What’s exciting about these trends is that they’re enabling organizations across industries to realize very different goals using their choice of technologies. And although all the trends depend on each other, research shows you can realize measurable benefits whether you adopt one or all five.

Review the report yourself and learn how you can refine your organization’s data and AI strategies by drawing on the collective insights, experiences, and successes of more than 800 global organizations.