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In his first interview as Qlik CEO, Saugata Saha shares what customers and partners can expect from his leadership and why collaboration across the Qlik ecosystem will be central to the opportunity ahead.
In the full conversation, Saugata also discusses:
Watch the full conversation:
As Saugata shares, the future is bright! To help you look ahead in style, we're sending Qlik sunglasses to our Qlik Community members 😎
Please join us in welcoming Saugata to Qlik and the Qlik Community. Share your reactions and thoughts with Saugata below, and we will send you a pair of Qlik shades (while supplies last).
Hi all, good evening,
I need to compare the cumulative sales between a year and the year before. This for many years stored in my database.
The sales had already been normalized by the month of each year.
This is the expression I created to generate the information for each year:
Aggr(RangeSum(Above(Sum(QTY), 0, RowNo())), [SALES YEAR, [YEAR/MONTH])
| YEAR/MONTH | SALES YEAR | QTY |
| (other year months) | ... | ... |
| 2026/01 | 2024 | 1 |
| 2026/02 | 2024 | 10 |
| 2026/03 | 2024 | 25 |
| 2026/04 | 2024 | 8 |
| 2026/05 | 2024 | 14 |
| ... | ... | ... |
| 2026/01 | 2025 | 0 |
| 2026/02 | 2025 | 3 |
| 2026/03 | 2025 | 12 |
| 2026/04 | 2025 | 11 |
| 2026/05 | 2025 | 7 |
| ... | ... | ... |
| 2026/01 | 2026 | 4 |
| 2026/02 | 2026 | 5 |
| 2026/03 | 2026 | 8 |
| 2026/04 | 2026 | 12 |
| 2026/05 | 2026 | 20 |
And this is the generated graphic:
What I need is to created a new graphic that shows, for each year, the % growth between that year vs its last year, using the same rule (acumulated sales for each month).
I have already tried many solutions here in the topics, but I couldn't get the right result.
For exemple: Aggr(RangeSum(Above(Sum(QTY) / Sum({<[SALES YEAR] = {"$(=Only([SALES YEAR])-1)"}>}QTY), 0, RowNo())), [SALES YEAR, [YEAR/MONTH])
Thanks in advance for any help.
From the moment you dive into the Qlik Community this month, Gwen Stamp's presence is hard to miss! An active member of the Women Who Qlik group, Gwen has spent more than 15 years leaving her mark on the Qlik ecosystem: showing up, sharing what she's learned, and helping others do the same. We're thrilled to name her our Featured Member for August 2026!
Gwen's relationship with Qlik goes back over 15 years, and she's seen the platform from nearly every angle along the way, working as a developer, consultant, platform owner, customer, educator, and community advocate. Today, she leads a Data & Analytics Center of Excellence within financial services, focusing on analytics strategy, governance, and enablement, helping people get more value from their data. She's also an adjunct professor teaching Business Statistics, driven by a passion for data literacy and helping others build the confidence to make data-informed decisions.
“We learn more when we learn from each other.”
— Gwen Stamp
Community has always been central to Gwen's Qlik journey. She notes that no matter how long someone has worked with Qlik, there's always another perspective, another problem solved, or a better approach waiting to be discovered. The Community has been a steady resource throughout her own journey, whether she's troubleshooting an issue, exploring a new capability, or keeping up with how the platform evolves. It's also given her a way to give back, sharing the successes and lessons she's picked up along the way. But for Gwen, it ultimately comes down to the people, some of the professional relationships and friendships she's built through the Community have lasted for years.
Outside of work, Gwen loves traveling and trying new things with her family. She's almost always learning something new or working on a creative project, and she enjoys roasting her own coffee. True to form, her curiosity doesn't stay contained to the day job, as she puts it, data and technology tend to sneak into her hobbies too, always wondering how things work and how they could work better.
Thank you for your years of contributions and engagement in the Qlik Community. Please join us in celebrating Gwen Stamp as our August 2026 Featured Member — leave a comment below!
The amount of change data that is being captured from source is very large. 200 million records or so for about 20 tables. And this is causing a latency of about 4 hrs or 5 hrs to commit this to target. Is there any way we can speed up this CDC commits? I am attaching a photo of the current monitor screen and my change capture settings. Please let me know of any tuning that can be performed. Source is SQL server (MS-CDC) and target is Snowflake.
Years ago I wrote the QlikView Systems Monitor (ugly but one of the most downloaded apps of its era). This is the same itch, scratched with 2026 tools.
Qlik Cloud has a native MCP endpoint for AI agents. Client-managed Qlik Sense on Windows has no official equivalent, so at Terenzi LLC we built one and open-sourced it under Apache-2.0. qseow-mcp exposes the QRS, Engine, and Proxy APIs as 44 agent tools across analyst, troubleshooting, developer, and admin personas. There is other community work in this space, and more of it is a good thing; our focus is the support and troubleshooting surface, full JWT-mode coverage, and a hard security posture.
The part we care most about is the troubleshooting loop. When a number is wrong, an agent can walk KPI to object to sheet to app to script to source, evaluating the governed definition at every step. In our demo estate it traces a $50M reconciliation mismatch to a double-loaded batch and ties it back out in seconds of tool time.
Two practical notes for anyone who tries it:
Published-app caveat: the engine hides a published app's script and lineage from user identities (GetScript returns empty, GetLineage denies, even for RootAdmin under a user identity). The workaround is copy_app and trace the owned copy. This is documented platform behavior, not a bug in the server, and it will bite any agent integration you build yourself.
The engine validates expressions but never proves pixels. A hypercube the engine accepts can still render a blank chart client-side (nebula.js default property blocks). The server ships a validate_sheet gate and all 12 supported chart types are render-verified against a real browser.
Repo: https://github.com/rmterenzi/qseow-mcp · Install: npm i qseow-mcp
Read-only by default, mutating tools gated at registration, credential redaction on every output path, certificate and JWT auth both covered. Independent project by Terenzi LLC (we are a Qlik partner; this is not a Qlik product). Issues and PRs welcome, especially reports from multi-node sites — everything so far is validated on single-node May 2026.
Hello,
I'm running QlikView server and publisher on a windows VM
Our license is renewed successfully as we confirmed with our contact in Qlik customer service management.
However i'm unable to apply the license, i have all the needed info (serial number, control number, LEF, owner information)
LEF should be correct as it was shared by our contact in Qlik.
Also tried to generate it from here with same results https://lef1.qliktech.com/manuallef/default.aspx
Tried restarting the QlikView windows services many times and applying the license again with no success.
worth noting the license for publisher is working fine with no issues.
Thanks in advance for any help or advise
We hope you've enjoyed our Make Your Data Work for AI discussions! Every organization is at a different stage of its AI journey.
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If you haven't already, watch the Make Your Data Work for AI story here!
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I'm not sure if this is the correct forum, but I'll give it shot. We are moving to Qlik Cloud from QlikView. I am attempting to create a custom theme, which does utilize a CSS file. I have been able to figure out the few things we need in a theme, but have stumbled on customizing a tab container. I want to change the tab background for the selected and unselected tabs, but this code does not work. This is my first exposure to CSS/JSON so I don't know what is keeping it from working. Any suggestions would be appreciated.
My JSON code:
{
"_inherit": true,
"_id": "custom-image-theme",
"name": "Custom Background Theme",
"description": "App theme featuring a global sheet background image.",
"type": "theme",
"version": "1.0.0",
"customStyles": [
{
"cssRef": "theme.css",
"classRef": "custom-theme"
}
],
"_variables": {
"@objectBackground": "#FFFFFF"
},
"backgroundColor": "@objectBackground",
"object": {
"backgroundColor": "@objectBackground"
}
}
My CSS code:
/* 1. Global font configuration
.qv-object * {
font-family: 'Helvetica Neue', Arial, sans-serif !important;
}*/
/* 2. Customizing sheet backgrounds and structures */
.qvt-sheet, .qv-panel-sheet {
background-image: url("QlikSense_SheetBackground.jpg") !important;
background-size: 100% 100% !important;
background-repeat: no-repeat;
background-position: top left;
}
/* 3. Main header and top-bar formatting */
#sheet-title.sheet-grid:after {
content: "";
display: block;
height: 30px; /* Adjust height to fit your logo */
width: 200px; /* Adjust width to fit your logo */
background-image: url('SMClogo285blue.png');
background-repeat: no-repeat;
background-size: contain;
background-position: left center;
position: absolute;
left: 20px; /* Adjust to move logo left or right */
top: 50%;
transform: translateY(-50%);
}
/* 4. Right-align the sheet title text */
.qv-object-header, .sheet-title-text {
text-align: right !important;
justify-content: flex-end !important;
font-size: 30px !important;
font-weight: bold !important;
color: #0071CE !important;
}
.qv-object .qv-object-title {
background-color: #FFFFFF !important;
padding: 8px 12px !important;
}
/* 5. Target the title text globally across standard visualization cards */
.qv-object-title .qv-object-title-text {
color: #000000 !important;
font-family: 'Helvetica Neue', Arial, sans-serif !important;
font-size: 16px !important;
font-weight: normal !important;
}
/* 6. Styling for the standard, unselected container tabs */
.qv-object-nav .tab-container .tab {
background-color: #b0afae !important;
color: #333333 !important;
}
/* Styling for the ACTIVE / SELECTED tab background color */
.qs-container .qsc-tab.active {
background-color: #0076ce !important; /* Set your hex color here */
color: #ffffff !important; /* Text color for active tab */
border-bottom: 2px solid #005699; /* Optional bottom indicator line */
}
Thanks in advance!
Hi everyone,
I’ve been a long-time advocate for QlikView, and even with the push toward newer platforms, I still find the Associative Engine in classic QlikView to be incredibly snappy for specific types of complex, local data discovery. I’m currently working on a project where I’m trying to optimize a fairly large .qvw for a small non-profit that is running their infrastructure on some older, repurposed enterprise hardware.
As someone who genuinely enjoys the challenge of "hardware recycling"—the idea of squeezing every bit of utility out of older machines rather than just buying into the latest cloud subscription—I’ve hit a bit of a performance wall that I'm hoping the veterans here can help me navigate.
The specific point I’m struggling with is the calculation time for objects using heavy Set Analysis and nested Aggr() functions. On my development machine, everything is fluid, but on the production host—an older workstation we’ve beefed up—the lag is becoming noticeable to the end users.
To give you some context on the environment, the server is running on a legacy DDR3 architecture. I’ve recently maxed out the board using PC & Server memory I had in my spare parts bin: specifically 8GB DDR3 1333MHz (PC3-10600) modules. I’ve managed to get the total capacity up to 64GB, which theoretically should be plenty of room for our 2GB (compressed) data model once it expands in-memory.
My personal insight from years of tinkering is that QlikView is almost entirely dependent on RAM throughput. However, I’m starting to wonder if the lower frequency of the PC3-10600 (1333MHz) is creating a latency bottleneck that no amount of capacity can fix. I’m seeing high CPU wait times during selections, even though the RAM isn't being fully utilized and the CPU usage isn't hitting 100%.
Specifically, I’m curious if anyone has experience deploying QlikView apps on older 1333MHz RAM. Does the Associative Engine suffer significantly from the lower bus speed of DDR3 compared to DDR4, or should I be looking more closely at my data model’s "Density" and "Cardinality" in the Document Analyzer? I’m trying to determine if it’s worth hunting down slightly faster RAM for this old motherboard, or if I just need to get more aggressive with script-level optimizations to reduce the reliance on complex UI expressions.
Has anyone else noticed a "stutter" in selection response times specifically tied to memory clock speeds rather than just raw volume?
Looking forward to hearing your thoughts on whether I should keep tuning the app or if the hardware has finally met its match.
Hi -
I'm getting the following error message in the logs:
WARN System.Engine.Engine StdServer: Writing dump and continue execution.I can't find where Qlik is creating the dump files.
Regards,
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