INTRODUCTION In an increasingly digitized world, organizations are generating and storing unprecedented volumes of data, a trend commonly referred to as Big Data. By leveraging Qlik’s powerful tools (QlikView and Qlik Sense), organizations can transform these vast and complex data sets into actionable insights.
UNDERSTANDING BIG DATA Big Data refers to extremely large data sets that can be analyzed to reveal patterns, trends, and associations, particularly relating to human behavior and interactions. While the definition of Big Data is fluid and can vary by organization, there are four key characteristics typically associated with Big Data: Volume, Velocity, Variety, and Veracity (the 4Vs).
THE CHALLENGE OF BIG DATA The challenge in Big Data lies not only in managing the sheer volume of data but also in being able to process and analyze it in a timely and meaningful way. With the increase in computational power and decrease in memory costs, it has become more feasible to process larger data sets. However, deriving useful insights from these data sets remains a challenge. Here’s where Qlik’s powerful business intelligence (BI) tools – QlikView and Qlik Sense come into play.
QLIK’S SOLUTION FOR BIG DATA Qlik’s tools offer a user-friendly interface for managing, processing, and visualizing Big Data. They leverage the Associative Engine that enables users to freely explore data by making associations across multiple data sources and discovering insights that may have been overlooked in traditional hierarchical or query-based data approaches.
Qlik has adopted a “Hybrid” approach to Big Data, which leverages both in-memory and on-disk capabilities, making it easy for users to interact with and explore their data. This approach allows the user to choose which part of the data should be in memory and what should remain on disk based on the analysis needs.
DIFFERENT METHODS FOR DIFFERENT AMOUNTS OF DATA AND COMPLEXITY Qlik offers several methods for handling Big Data scenarios, including:
In-memory:
Qlik’s associative engine optimizes speed in RAM by compressing data to 10% of its original size, making it easier to handle increasing amounts of data while maintaining high performance.
Segmentation:
This involves dividing a single Qlik application into multiple applications to optimize performance, security, scalability, simplicity, and service.
Chaining:
Refers to linking different Qlik applications and maintaining users’ choices between the linked applications.
On-demand creation:
Known as On-Demand Application Generation on Demand (ODAG), this method allows users to automatically create a target application for analysis each time they select a slice of a very large data source.
Other methods:
These include integration with partner technologies, tools, or developing custom applications using JavaScript and Qlik’s APIs.
Qlik’s platform is designed to be open and comes with a number of built-in and third-party capabilities to connect to Big Data repositories, including ODBC connectivity, partner connectivity, and certified ODBC driver providers.
QLIK: BRIDGING THE LAST MILE OF BIG DATA Qlik has effectively addressed the “last mile” problem of Big Data. Unlike other vendors who focus mostly on data processing, Qlik focuses on the final steps of bringing data to the end-user in a meaningful and insightful way. The goal is to make Big Data accessible and valuable to every user, not just data scientists, by enabling interactive and dynamic data exploration in combination with traditional data sources. This approach ensures that organizations can extract maximum value from their Big Data investments.
On-Demand Application Generation (ODAG) in Qlik Sense: Empowering Dynamic Data Exploration
Introduction In the realm of data analytics, the ability to quickly and efficiently explore vast amounts of data is crucial for making informed decisions. Qlik Sense, a powerful data visualization and exploration tool, offers a unique feature called On-Demand Application Generation (ODAG). ODAG enables users to dynamically create targeted applications for in-depth analysis, allowing them to uncover valuable insights from complex and extensive data sources. In this article, we will delve into the concept of ODAG in Qlik Sense and explore how it revolutionizes the way organizations leverage their data.
Understanding ODAG ODAG is a methodology developed by Qlik to address the challenge of navigating large and diverse datasets effectively. It facilitates a step-by-step approach to data exploration, allowing users to analyze specific slices or segments of data without overwhelming their system resources or compromising performance. ODAG leverages Qlik Sense’s associative data model, which enables users to make dynamic selections and explore data in any direction, facilitating a truly interactive and exploratory analytics experience.
The Benefits of ODAG
Dynamic Exploration:
ODAG empowers users to explore different portions of a data source without having to develop a new application each time. By selecting specific subsets of data, such as time periods, customer segments, or geographies, users can trigger the immediate creation of a targeted application that contains only the relevant detailed data. This flexibility allows for on-the-fly analysis, empowering users to uncover insights without being limited by predefined application structures.
Efficient Resource Utilization:
Since ODAG applications are created on demand, only the requested portion of detailed data is processed in memory at any given time. This approach optimizes resource utilization, making it possible to analyze and visualize data from massive datasets without overwhelming system resources. As a result, users can explore and interact with large datasets without compromising performance.
Reduced Development Overhead:
Traditional application development often requires significant time and effort to design, build, and maintain. ODAG eliminates the need for developing numerous applications by enabling users to create targeted applications on the fly. This reduces development overhead and accelerates the time-to-insight, allowing users to focus on analysis rather than application creation.
Enhanced Security and Governance:
ODAG applications inherit the security rules and governance policies defined in Qlik Sense. This ensures that only authorized users can access the detailed data relevant to their selections, maintaining data integrity and security. IT administrators can control access to both summary information and detailed data, ensuring compliance with data governance regulations.
Implementing ODAG in Qlik Sense Implementing ODAG in Qlik Sense involves two primary components: the selection application and the target application.
Selection Application: The selection application acts as a hub where users can make high-level selections to define their analysis scope. Users can choose subsets of data such as specific time periods, product categories, or customer segments. Based on these selections, the selection application triggers the creation of a target application.
Target Application: The target application is dynamically generated based on the user’s selections in the selection application. It contains detailed data specific to the chosen subsets. Users can then explore the detailed data in any direction using Qlik Sense’s powerful associative engine, enabling them to uncover valuable insights and make data-driven decisions.
Conclusion On-Demand Application Generation (ODAG) in Qlik Sense revolutionizes the way organizations approach data exploration and analysis. By empowering users to dynamically create targeted applications based on their selections, ODAG enhances the efficiency, flexibility, and depth of data exploration. Users can unlock valuable insights from large and complex datasets, while IT administrators can ensure data security and governance. With ODAG, Qlik Sense provides a powerful tool for organizations to maximize the value of their data assets.
With ODAG, users can easily navigate through extensive datasets without sacrificing performance or overwhelming system resources. They can focus on exploring specific subsets of data that are relevant to their analysis, enabling a deeper understanding of complex data relationships and uncovering valuable insights.
ODAG also promotes a self-service analytics culture within organizations. Users can independently create and explore targeted applications without relying on IT or data specialists to develop customized applications for each analysis requirement. This empowers business users to access and analyze data in real-time, fostering a data-driven decision-making process throughout the organization.
Furthermore, ODAG enhances collaboration and knowledge sharing among users. As users create targeted applications based on their specific analysis needs, these applications can be easily shared with colleagues or teams, fostering a collaborative environment where insights and discoveries can be shared across the organization.
Qlik Sense’s ODAG functionality can be extended and customized to fit the unique requirements of different industries and organizations. Through the use of APIs and integration with external systems, organizations can incorporate additional data sources, automate data refreshes, or implement specialized security protocols to align with their specific needs.
As the volume and complexity of data continue to grow, ODAG in Qlik Sense provides a scalable and efficient solution for organizations to leverage their data assets effectively. It enables users to navigate through vast datasets, generate insights, and drive better decision-making processes.
Introduction of On-Demand Application Generation (ODAG) in Qlik Sense represents a significant advancement in the field of data analytics. By allowing users to dynamically generate targeted applications for data exploration and analysis, ODAG empowers organizations to harness the full potential of their data resources. With its flexible and efficient approach, ODAG facilitates a deeper understanding of data relationships, enhances collaboration, and enables informed decision-making. Qlik Sense, combined with ODAG, is poised to revolutionize the way organizations leverage data to gain a competitive advantage in today’s data-driven world.
Qlik Sense and Big Data sources
Qlik Sense is designed as an open platform and offers connectivity to various big data sources through drivers and connectors. Here are some popular big data sources with their corresponding drivers for Qlik Sense:
Apache Hadoop: Qlik Sense provides connectivity to Hadoop through ODBC drivers. You can use Qlik’s built-in ODBC connectivity to connect to Hadoop distributions such as Cloudera, Hortonworks, and MapR.
Apache Hive: Hive is a data warehousing infrastructure built on top of Hadoop. Qlik Sense offers ODBC connectivity to Apache Hive, allowing you to analyze and visualize data stored in Hive.
Cloudera Impala: Impala is an open-source massively parallel processing (MPP) SQL query engine for Apache Hadoop. Qlik Sense supports connectivity to Cloudera Impala through ODBC drivers.
Apache Spark: Spark is a fast and general-purpose cluster computing system that provides in-memory processing capabilities. Qlik Sense offers a Spark connector, enabling you to connect to Spark and leverage its distributed computing power for analytics.
Amazon Redshift: Redshift is a fully managed data warehousing service provided by Amazon Web Services (AWS). Qlik Sense provides an ODBC connector for Amazon Redshift, allowing you to access and analyze data stored in Redshift.
Google BigQuery: BigQuery is a serverless, highly scalable data warehouse provided by Google Cloud. Qlik Sense offers a connector for Google BigQuery, enabling you to connect and analyze data directly from BigQuery.
Microsoft Azure Synapse Analytics (formerly Azure SQL Data Warehouse): Azure Synapse Analytics is an integrated analytics service provided by Microsoft Azure. Qlik Sense provides connectivity to Azure Synapse Analytics using ODBC drivers, allowing you to analyze data stored in Synapse Analytics.
MongoDB: MongoDB is a popular NoSQL database that provides flexible document storage. Qlik Sense offers a connector for MongoDB, enabling you to connect and visualize data stored in MongoDB databases.
Elasticsearch: Elasticsearch is a distributed search and analytics engine. Qlik Sense provides a connector for Elasticsearch, allowing you to connect and explore data indexed in Elasticsearch.
These are just a few examples of big data sources supported by Qlik Sense. Qlik also partners with various vendors and offers a marketplace for connectors developed by third-party partners, expanding the range of data sources that can be integrated with Qlik Sense.