5 Reasons to Consider Qlik Data Integration

In today's competitive market, companies face the challenge of processing large amounts of data efficiently to stay ahead. By proactively approaching data analysis, you can make informed decisions and maintain your competitive advantage. However, integrating data from different systems and platforms can be complex and costly, resulting in delays and operational inefficiencies. In this blog, BI consultant Rodrigo Gomes gives you five reasons why you should consider choosing Qlik Data Integration to tackle these data integration challenges and streamline your data management.

Imagine your CEO entrusts you with the important project of enabling all analysts to access sales and financial data through a single centralized cloud platform. The aim here 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 establish a reliable and simple 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 one 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, enabling timely insights and informed decision-making. Embrace the path to data democratization, simplify your infrastructure, and unleash the true power of your data by using Qlik Cloud Data Integration.

#1 Wide variety of source connectivity

Imagine there are various systems and software within your company that manage 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's crucial to understand each of these databases in order to establish effective connections and extract the required data.

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

Below you'll find 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 through Change Data Capture (CDC)

Change Data Capture (CDC) revolutionizes how we monitor and capture changes in data within 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.

In addition, CDC works based on log-based replication. You don't need to install a software agent on your source systems for this. These techniques ensure minimal impact on the source system, which is important if you can't afford downtime or overload from running queries on the database.

#3 Data modeling without scripting knowledge

To use data effectively for BI analytics applications, a different data structure and aggregation level is needed than how it's 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 transactional data, such as a sales order number, product, and quantity, while the dimensions contain the attributes linked to these transactions, such as product name, category, and group.

QDI uses 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 by the tool, ensuring these best practices are followed, resulting in optimized data marts, without requiring extensive knowledge of scripting or job scheduling.

#4 Wide 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's generally recommended to have a landing zone where data arrives in raw form before being 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 effectively access and work with the data.

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

#5 Cost savings

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

When developing an end-to-end data integration project, a diverse range of technical skills is often needed, 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. Moreover, you can save significant investment in infrastructure while reducing operational costs, resulting in a more cost-effective and efficient data integration process.

Conclusion

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

Written by Rodrigo Gomes
Business Intelligence Consultant
E-mergo