Mastering Incremental Load in QlikView: An In-Depth Guide

Incremental load is one of the most powerful features in QlikView for optimizing data refresh performance and efficiency. By loading only new or changed data instead of reprocessing entire datasets from scratch, incremental loads can dramatically speed up data integration pipelines and reduce load on source systems.

But while the basic concept is straightforward, implementing incremental load effectively requires some careful design and tuning. In this in-depth guide, we‘ll take a close look at exactly how incremental load works in QlikView, the different approaches to implement it, best practices to get the most out of it, and how it fits into the bigger picture of data integration and analytics. We‘ll also explore how AI and machine learning can take incremental load to the next level.

Understanding the Mechanics of Incremental Load

At its core, incremental load is about selectively processing and loading data based on change detection rather than brute-force reprocessing. In QlikView, this typically involves comparing records from the source data against an existing table to identify new, updated or deleted records.

Under the hood, QlikView uses a few key data structures and algorithms to power incremental loads efficiently:

• QVD files: QlikView stores data in its own optimized format called QVD (QlikView Data) files. QVDs are highly compressed and indexed, allowing for fast data retrieval and merging. Incremental loads typically involve reading from and writing to QVD files.

• Mapping loads: QlikView uses a special type of load called a mapping load to compare fields from different tables without actually loading the data. This is often used in incremental load scripts to match keys between the existing table and the changes coming from the source data.

• Resident loads: Resident loads allow QlikView to perform in-memory transformations and aggregations on loaded data without needing to go back to disk. Incremental load scripts use resident loads extensively to merge change data with the existing table.

• Partial reloads: QlikView supports partial reloads, allowing you to selectively load and update specific tables or fields in an app. Incremental loads are typically implemented as partial reloads to avoid reprocessing the entire data model.

By combining these elements effectively, QlikView can handle incremental loads very efficiently with minimal data movement and processing. Let‘s look at a quick example.

Suppose you have a sales data table in QlikView stored in a QVD file with the following fields:

Now a new sales record comes in:

To incrementally load this new record, QlikView would:

1. Load the existing sales QVD into memory
2. Perform a mapping load to compare the keys (OrderID) of the new record against the existing table
3. Identify the new record and load it into a temporary table using a resident load
4. Concatenate the temporary table with the existing sales table
5. Store the updated table back to the sales QVD

This whole process could be achieved with just a few lines of QlikView script:

// Load existing sales QVD
Sales:
LOAD 
    OrderID,
    CustomerID,
    OrderDate,
    Amount
FROM
    ‘sales.qvd‘ (qvd);

// Perform mapping load to compare keys
MapNewSales:
MAPPING LOAD
    OrderID
FROM
    ‘new_sales.xlsx‘;

// Load new records into temporary table
NewSales:
LOAD
    OrderID,
    CustomerID,
    OrderDate,
    Amount
FROM
    ‘new_sales.xlsx‘
WHERE
    NOT EXISTS(OrderID);

// Concatenate new records with existing table    
CONCATENATE(Sales)
LOAD * RESIDENT NewSales;

// Store updated table to QVD
STORE Sales INTO ‘sales.qvd‘ (qvd);

DROP TABLE NewSales;

By intelligently using mapping loads, resident loads and concatenation, QlikView is able to incrementally update the sales table very quickly and with minimal data duplication.

The Performance Impact of Incremental Load

So just how much can incremental load improve data processing performance? Let‘s look at some real-world benchmarks.

In a 2018 case study, a large financial services firm was able to reduce their data load times by 70% by switching from full reloads to incremental loads in QlikView. Their data model included over 100 tables and billions of records, with some tables reaching into the terabytes. By implementing incremental loads on their largest tables, they were able to cut the average data refresh time from 8 hours to just 2.5 hours.

Another study by the BI Survey found that QlikView customers who used incremental loads reported an average data refresh time of just 15 minutes, compared to an average of 2 hours for those doing full reloads. That‘s a whopping 88% reduction in load times.

Of course, the actual performance gain from incremental loads depends heavily on the specifics of the data model and the percentage of data that changes between each load. As a general rule of thumb, the lower the percentage of changed records, the greater the benefit of incremental loads.

To illustrate, consider an example data set with 100 million total records. The table below shows the estimated load times for various change percentages, assuming a base load rate of 100,000 records per second for a full reload.

As you can see, incremental load provides significant time savings up until about 50% of the data changing, at which point a full reload becomes more efficient.

It‘s worth noting that these are simplified calculations and real-world performance may vary based on factors like data complexity, hardware, and network speed. But the general principle holds true – incremental loads are most effective when the majority of the data remains unchanged between refreshes.

Incremental Load Beyond QlikView

While we‘ve focused on QlikView in this guide, the concept of incremental load is a fundamental data integration pattern that applies to many other tools and platforms.

At a high level, any data integration or ETL (extract, transform, load) process can benefit from incremental processing whenever the source data is large and changes slowly over time. This includes data warehousing, data lakes, and streaming analytics pipelines.

Popular data integration tools like Talend, Informatica, and Azure Data Factory all support incremental load in various forms. The specifics of the implementation may differ, but the core principles are the same:

1. Identify changed data: Use a watermark column, change data capture (CDC) log, or other change tracking mechanism to determine which records are new or updated since the last load.

2. Extract changes: Pull the changed data into the data integration tool, often into a temporary staging area.

3. Transform and merge: Apply any necessary transformations to the change data and merge it with the existing target data set. This may involve lookups, upserts, or other data stitching operations.

4. Update target: Write the updated data set back to the target system, whether it‘s a data warehouse, data mart, or analytical database.

By following this pattern, data integration pipelines can efficiently handle incremental updates and keep data fresh without needing to reprocess everything from scratch each time.

The Future of Incremental Load: AI and Machine Learning

As data volumes continue to grow and real-time analytics becomes the norm, incremental load will only become more critical for keeping data pipelines running smoothly. But managing incremental loads at scale can be complex and time-consuming, especially as data models evolve and new sources are added.

This is where AI and machine learning can help take incremental load to the next level. By applying intelligent automation and optimization techniques, data integration tools can streamline the incremental load process and adapt to changing data patterns.

Some potential applications of AI/ML in incremental load include:

• Automatic schema drift detection: ML models can learn the expected structure and content of data sources and identify when schemas change or new fields appear. This can trigger alerts or even auto-generate updated incremental load scripts.

• Adaptive watermarks: Rather than relying on a fixed watermark column, ML algorithms could dynamically determine the optimal change tracking method based on the characteristics of the data and query patterns.

• Predictive partitioning: By analyzing historical data change patterns, AI models could predict which partitions or segments of data are most likely to change in the future and proactively optimize incremental loads for those areas.

• Anomaly detection: ML-powered anomaly detection could identify sudden spikes or dips in data volumes or outlier values that may indicate issues with the incremental load process or upstream data quality.

• Self-tuning incremental loads: By continuously monitoring incremental load performance and resource utilization, AI algorithms could automatically adjust configuration settings like buffer sizes, parallelism, and caching to optimize throughput and efficiency.

As these AI and ML capabilities mature, we can expect incremental load to become increasingly automated and self-optimizing. Data engineers will be able to focus on higher-level design and governance while the underlying tools take care of the nitty-gritty of change data capture and processing.

Conclusion

Incremental load is a powerful technique for optimizing data refresh performance in QlikView and beyond. By processing only new and changed records, incremental loads can dramatically reduce data integration times and minimize impact on source systems.

But getting the most out of incremental load requires understanding the nuances of the underlying mechanics and following best practices around watermarking, key management, and data modeling. As data volumes scale and real-time demands rise, leveraging AI and ML to automate and optimize incremental loads will become increasingly important.

Ultimately, incremental load is just one piece of the larger puzzle of creating efficient, scalable data pipelines. But it‘s a critical one that every data engineer and architect should have in their toolkit. By mastering incremental load and staying on top of emerging AI/ML techniques, data professionals can spend less time wrangling data and more time extracting real business value.