Digital data is all around us. As per DataReportal, a total of 5.19 billion people around the world were using the internet at the start of Q3 2023, equivalent to 64.5 percent of the world’s total population. Internet users continue to grow too, with the latest data indicating that the world’s connected population grew by more than 100 million users in the 12 months to July 2023. If utilized correctly, data offers a vast number of opportunities to individuals and companies looking to improve their business intelligence, operational efficiency, profitability, and growth over time.
With this much amount of data present around us which is growing exponentially, it’s crucial to work with the right reporting tools to segment, curate, and analyze large data sets. For this purpose, ad hoc reporting is essential. Let’s understand further about ad-hoc reporting.
What is Ad Hoc Reporting?
You can generate one-time reports with real data in the form of dynamic dashboards. Users can easily create ad hoc reports using self-service business intelligence tools.
Ad hoc reporting forms a critical part of a business as data reports are generated daily, weekly, bi-monthly, or monthly- depending on an organization’s workload. By offering insights that help in adding value and making informed decisions in the decision-making process, ad-hoc reporting forms a vital part of any business, brand, or organization’s growth and sustainability.
Typically, these reports are developed by the IT department using SQL. But thanks to some tools and platforms like Qubole, non-technical business users can access these insights with the help of ad-hoc reporting, which provides quick reports for single use without generating complicated SQL queries.
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Ad Hoc Reporting Tools
Data is continually growing more important, and the rise in data volumes and importance means organizations will eventually struggle to manage their data. Ad hoc reporting tools offer a number of benefits that enable businesses to get as much value as they can from the information they collect.
Let’s look at the benefits of using these ad-hoc data reports:
- Reduced IT workload: Since ad-hoc reports are self-service in nature, meaning one doesn’t need to learn the complex SQL language to create them, ad-hoc reporting catalyzes the report creation process by allowing end-users to work with customized reports on niche areas of the business. This results in saving time and cost, while minimizing any potential interdepartmental roadblocks.
- Easy to use: Given their intuitive and visual nature, ad hoc data analysis platforms or dashboards allow users to make decisions and roll out initiatives that help improve their business without navigating through the daunted streams of data.
- Ensures flexibility: In a constantly changing business environment, ad hoc analytics offers an interactive reporting experience, empowering end-users to make alterations in real-time and customize their needs and goals accordingly.
- Saves time and costs: The modern ad hoc reporting interface is designed to be simple, intuitive, and powerful. The intuitive nature helps users create interactive visuals without waiting for a professional analyst or the IT department. This self-service business intelligence nature enables a data-driven system to save countless working hours and costs since users don’t have to wait for reports. In fact, it increases productivity as the team can immediately manipulate formulas and avoid multiple spreadsheets to consolidate data.
- Completely customizable: Organizations must consider the possibilities of customization that ad hoc reporting platforms offer. Some tools already have a built-in dashboard that can be used as per one’s requirements. This can help you save even more time and focus on your business needs. Qubole provides users with even more freedom if they are looking for modern software solutions.
- Empowers staff: Ad hoc reporting solutions empower people to access reports as needed by providing basic intuitive features for nontechnical people and advanced ad hoc tools for data professionals.
- Agile decision-making: With the changing business environments, businesses must be able to adapt and evolve quickly to stay competitive. Ad hoc reporting makes it possible to answer questions on demand so businesses can make decisions faster.
- Encourages collaboration: Ad hoc reporting tools encourage collaboration by making it not only easy to create reports but to organize and share them with other teams as and when demanded.
Ad Hoc Reporting Examples
Many organizations across industries rely on ad hoc reporting for informed decision-making. Take a look at how different sectors put them to use:
- Sales: With ad hoc reporting, sales managers can tap into specific data, such as creating reports that show how many items were sold over a certain period or understanding sales outcomes based on specific scenarios, such as location.
- Healthcare: Physicians and healthcare administrators must be able to generate data reports and analyses at will, for which they use ad-hoc reporting. For example, a hospital with suddenly higher readmission rates can run an ad hoc analysis to discover what might be the underlying causes. This can help the hospital come up with a solution to offer better care to its patients.
- Human Resources: From salary to leave balance to benefits and performance information and more- Organizations collect huge amounts of employee data. With ad-hoc reporting tools, HR departments can spot deficiencies that, when resolved, can improve employee satisfaction and engagement.
- Finance: Ad hoc data reporting makes it easier for finance teams to drill down into any combination of financial data at will, such as AR and AP figures, metrics, key performance indicators (KPIs), and other business data which would help them review discounting or profitability of a new product, or measure the expenses of a particular region in a given quarter.
- Retail: In order to understand what affects sales volume so that the stores can optimize inventory levels to prevent dead stock, retail organizations need to bank upon ad hoc reports. These reports can show specific times of low sales volume, thereby helping managers determine whether, for instance, they should scale back inventory or reduce labor hours.
Presto Ad Hoc Reporting
Presto is a widely embraced distributed SQL engine for data lake analytics. Presto can also execute ad-hoc querying of data, which helps to solve challenges regarding time to discover and the amount of time it takes to perform ad hoc analysis. Furthermore, it also provides users with new features like the disaggregated coordinator, Presto-on-Spark, scan optimizations, a reusable native engine, and a Pinot connector enabling added benefits around performance, scale, and ecosystem.
Presto also provides many advantages for organizations of different sizes. Particularly, it boasts of the ability to query data such that it reduces the amount of time required by data engineers to build complex ETLs. This implies that clients or customers can be provided with faster replies to their questions.
Qubole’s Presto-as-a-Service is primarily intended for Data Analysts who need to transform business questions into SQL queries. Since these questions are often ad-hoc, trial and error are involved to some degree; achieving the final results may require a series of SQL queries. By reducing the response time of these queries, the platform can cut down on the time to insight and prove to be extremely beneficial for the business.
Spot Node Management
Spot Nodes on AWS and Preemptible VMs on GCP are extremely popular among customers who find them very efficacious in the reduction of cloud costs. Qubole helps Presto customers utilize Spot nodes without surrendering reliability through built-in features that elegantly handle Spot interruptions.
Presto Scheduler Improves Cache Reads by Up to 9x in RubiX
When Presto queries are run in a RubiX-enabled cluster, RubiX ensures that the data gets cached in the worker node’s local disk when it executes the split that reads the data from cloud storage. Once the data is available in a worker node, subsequent reads of that data will be served from that worker node’s disk instead of using the cloud source. When a split executes on the same node where the required data resides, it is referred to as a ‘cached read’. On the other hand, when a split executes on a different node in the cluster, it is referred to as a ‘non-local read’; in such a case, the data is read over the cluster network. Because cached reads are served locally without any network transfer, they are faster than non-local reads. Therefore, it is desirable to have a higher number of cached reads than non-local reads.
RubiX provides hints to the Presto scheduler about the locality information of the split, and it is up to the Presto scheduler to assign splits as per its assignment policies.
Configuring Tableau using Qubole Presto Connector
There are three types of Qubole Presto Connectors available to configure Tableau:
- Custom Qubole Presto Connector (JDBC) – For Tableau Version 2019.1, 2019.2, and, 2019.3
- Inbuilt Qubole Presto Connector (ODBC) – For Tableau Version 2019.4, 2020.1, and 2020.2
- Inbuilt Qubole Presto Connector (JDBC) – For Tableau Version 2020.3 or Later
Tableau is a business intelligence tool that helps non-technical data analysts transform raw data into interactive graphics and customized dashboards. Organizations use Tableau for visualizing data, turning complex information into actionable insights.
To define the connectivity between Tableau and Qubole, you must specify the Qubole API Token (which Qubole uses to authenticate access), the cluster name (cluster Label), and the endpoint (Qubole platform in the cloud provider where the customer has its Qubole account). The first time a query, a dashboard, or a report is run, Qubole authenticates the Tableau user and starts the big data engine (Presto or Hive) that Tableau requires, if it is not already running. After that, Tableau sends SQL commands through the ODBC/JDBC driver that Qubole passes to the right cluster. Qubole manages and runs the cluster with the right number of nodes only when required, thus saving users up to 40% in infrastructure costs.
Using Qubole through Looker
The Qubole Data Service (QDS) and Looker’s analytics data platform integration gives line of business users instant access to automated, scalable, self-service data analytics without having to build and maintain on-premises infrastructure.
Using Qubole and Looker, organizations can implement self-service data exploration and visualization without having to rely on or overburden the Data Science team. Qubole automatically provisions, manages, and scales the big data infrastructure in the cloud, freeing the Data Engineering team and Data Scientists from the tedious task of managing it. Analysts can use Looker to make the data in Qubole available to everyone across an organization regardless of technical ability for data exploration, visualization, or sharing.
Looker users connect directly to Qubole using Qubole’s JDBC drivers. Once connected, analysts can leverage Looker’s modeling language, LookML to define and share a data model across the company. Looker enables each analyst to prototype new work and push to production, so business users can leverage this model to create and edit their own reports and dashboards. Effectively, Looker enables self-service for anyone to create custom reports from data processed in Qubole. The first time a query or a report is run, Qubole authenticates the Looker user and starts the big data engine (Presto), if it is not already running. After that, Qubole scales and manages the big data engine, running it only for the required time, thus saving users up to 40% in infrastructure costs.
Here are the major benefits of the Looker-Qubole integration:
- Permits users to choose their preferred Big Data engine (Presto) to connect Looker and Qubole.
- Automatically provisions Qubole clusters when a Looker user runs a report or query.
- Automatically scales Qubole clusters up and down, based on the number of Looker ad-hoc analyses and reports, saving cost by not requiring to have clusters running all the time.
- Reduces data movement by leveraging Qubole directly
- Enables self-service analysis in Qubole regardless of technical ability.
- Authenticates Looker connections through Qubole with secure communication.
- Saves the query history in Qubole to allow auditing of Looker queries.
Why Presto on Qubole for Ad-hoc Reporting
Qubole has been providing a Presto service as part of its open data lake platform since 2014. In the past few years, Presto has set the standards for fast analytical processing in modern cloud data lake architectures.
Besides, Presto service on Qubole offers this superlative performance at a lower cost than its competition by leveraging distinguished features around the utilization of low-cost computing with better reliability and workload-aware autoscaling. With Presto, businesses can operate ad-hoc queries of data to tackle challenges around time to discover and the amount of time it takes to perform ad hoc analysis, therefore saving huge amounts of costs.
To summarize, Presto, a widely embraced distributed SQL engine for data lake analytics, can also execute ad-hoc querying of data. It helps to solve challenges regarding time to discover and the amount of time it takes to perform ad hoc analysis. Furthermore, it also provides users with new features like the disaggregated coordinator, Presto-on-Spark, scan optimizations, a reusable native engine, and a Pinot connector enabling added benefits around performance, scale, and ecosystem.
Presto also provides many advantages for organizations of multiple sizes. Particularly, it has the ability to query data such that it reduces the amount of time required by data engineers to build complex ETLs. This implies that clients or customers can be provided with faster replies to their questions.
Qubole’s Presto-as-a-Service is primarily intended for Data Analysts who need to transform business questions into SQL queries. Since these questions are often ad-hoc, trial and error are involved to some degree; achieving the final results may require a series of SQL queries. By reducing the response time of these queries, the platform can cut down on the time to insight and prove to be extremely beneficial for the business.
