5 Reasons to Consider Qlik Data Integration

In today's competitive market, companies face the challenge of processing large volumes of data efficiently to stay ahead. By taking a proactive approach to data analysis, you can make informed decisions and maintain your competitive edge. However, integrating data from different systems and platforms can be complex and costly, leading to delays and operational inefficiencies. In this blog, BI consultant Rodrigo Gomes gives you five reasons to consider Qlik Data Integration to tackle these data integration challenges and streamline your data management.

Imagine your CEO entrusts you with an important project: enabling all analysts to access sales and financial data through a single, centralized cloud platform. The aim is to democratize the data by providing a single source of truth and strengthening the data infrastructure, while eliminating the need for complicated skills and technologies. The main goal is to create a reliable, straightforward solution that enables efficient data management.

What is Qlik Data Integration?

Qlik Data Integration (QDI) is a robust data integration platform designed to enable seamless data movement and integration across diverse systems and sources within a single, user-friendly platform. By automating the creation of your data lake and data warehouse, you save valuable time that would otherwise be spent on manual processes. It also enables organizations to transform and deliver data in near real time, making timely insights and informed decision-making possible. Embrace the path to data democratization, simplify your infrastructure and unleash the true power of your data with Qlik Cloud Data Integration.

#1 Wide variety of source connectivity

Imagine there are various systems and software in your company managing different processes. These systems store large amounts of data in different databases. The challenge is to connect to these systems, capture historical data and process new changes.

These databases are called "sources", and because of their diverse types and technologies, it is crucial to understand each of them in order to establish effective connections and extract the required data.

QDI already supports more than 250 connectors and, through Connector Factory, promises to add another 100 new connectors in 2023.

Below is a selection of the most common sources supported by QDI:

• RDBMS: Oracle, SQL, DB2, MySQL, Sybase, PostgreSQL
• Hadoop: Hortonworks, Cloudera
• Data warehouses: Exadata, Teradata, IBM Netezza, Vertica, Pivotal, SQL Data Warehouse
• Legacy: IMS/DB, DB2 z/OS, RMS, VSAM
• SAP, Mainframe

#2 Real-time data loading with Change Data Capture (CDC)

Change Data Capture (CDC) revolutionizes how we monitor and capture changes to data in a database. By delivering these changes to downstream processes or systems in near real time, it ensures smooth synchronization and gives you constant access to the most accurate and up-to-date data.

CDC also works on the basis of log-based replication. You do not need to install a software agent on your source systems. These techniques ensure minimal impact on the source system, which is important if you cannot afford downtime or overload caused by running queries on the database of the source system.

#3 Data modeling without scripting knowledge

To use data effectively for BI analytics applications, you need a different data structure and level of aggregation than the way it is stored in the source system's databases. This is where data modeling comes in. Data modeling is the process of structuring the tables within your data system into facts and dimensions. The facts contain transaction data, such as a sales order number, product and quantity, while the dimensions contain the attributes linked to these transactions, such as the product name, category and group.

QDI takes a unique design-driven approach to modeling, integrating data modeling rules based on industry standards such as the Fact x Dim method popularized by Ralph Kimball. With QDI, users are guided through the tool, which ensures these best practices are followed and results in optimized data marts, without requiring extensive knowledge of scripting or job scheduling.

#4 Large selection of possible target systems

To manage your data effectively, you need a separate database in which you can store both historical information and future updates. In practice, it is often recommended to have a landing zone where data arrives in raw form before it is processed and moved to other storage layers (databases). These storage layers are also called the "target". The target needs a secure and scalable storage solution that enables data teams to access and work with the data effectively.

QDI can deliver data to the most popular cloud databases and lakes, such as Google Cloud, Amazon Redshift, Microsoft Azure Synapse, Databricks, Snowflake and also MSSQL (on-premises). Thanks to this wide diversity of possible targets, you can choose the vendor that best fits your preferences and budget.

#5 Saving costs

The last crucial aspect of the entire data integration process that must not be overlooked is cost. It is important to consider both the costs of developing the solution with skilled teams and the costs of hosting it in a secure and scalable environment.

Developing an end-to-end data integration project often requires a diverse set of technical skills, including expertise in programming languages, microservices, DevOps and analytics. Every part of the process requires attention and skilled people to ensure its success.

On the other hand, hosting the solution on a robust server and managing numerous services and security measures can lead to high hosting and infrastructure costs.

This is where QDI comes in. With Qlik, you have access to a single software-as-a-service (SaaS) platform where all your data integration projects can be housed in one convenient location. This means you need fewer diverse technical skills to bring the project to a successful conclusion. You can also save significantly on infrastructure investments while reducing operational costs, resulting in a more cost-effective and efficient data integration process.

Conclusion

In summary, to process the immense amount of data generated every minute efficiently and maintain a competitive advantage, it is essential to embrace scalability, flexibility and cost savings. By moving to the cloud with Qlik Data Integration, you can keep access to the same robust data processing capabilities as before, regardless of the size or complexity of the source database. This seamless integration ensures that up-to-date information is immediately available without affecting production systems and facilitates the automatic delivery of actionable insights through Qlik Sense Analytics.

Switching to Qlik Data Integration?

Want to know more about what a switch to QDI can do for you? Then schedule an appointment right away using the button below.

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