Share your experience using Analance

We use the product for data transformation and analysis. We primarily use it for data preparation, analysis, and sometimes generation. So, it covers a wide range of tasks beyond just loading data.

Alteryx has a good UI. We use it frequently in our projects. The tool comes with drag-and-drop features and is easy to understand for business needs. One situation where Alteryx's advanced analytics capabilities were particularly beneficial for us was during a forecasting project. Unlike Python, which requires coding, Alteryx simplifies the process significantly. With Alteryx, users can adjust parameters within the user interface without writing any code. 

Its user interface is one of the tool's best aspects. Once we became familiar with it, we found that tasks can be completed within seconds or minutes, especially for small data analyses. The tool's computation time is also commendable.

We have integrated the product with Tableau and Python. 

There have been some issues with licensing, particularly with the increased prices. This has led some companies, including ours, to consider reducing the number of licenses or potentially discontinuing their use of Alteryx.

Since most of our team members work with Alteryx, deploying workflows to clients who may not have licenses became a hurdle. Every user needed a license to run the workflows, which could pose difficulties, especially for clients who only needed occasional access for small data analyses. Unlike other software like Microsoft Power BI, which offers free trials or user licenses for basic functionality, Alteryx doesn't provide such options, potentially limiting its accessibility for occasional users.

It should improve scalability. 

I have been using the product for more than ten years. 

I rate the tool's stability a ten out of ten. 

I rate the solution's scalability four to five out of ten. 

The tool's deployment can be completed in a day or two. We have 24 engineers to handle it. 

The solution's deployment was done in-house. 

I rate the tool's pricing a two out of ten. 

Overall, I would recommend Alteryx for complex data analysis needs. We are considering moving to Tableau Prep. 

The solution is great as a standalone product. However, there are certain add-ons provided by Alteryx that are very useful but come with additional costs on top of the licensing fees. This aspect can be a bit of a downside. So, overall, considering this factor, I would rate Alteryx at around seven out of ten.

There are numerous benefits to using Alteryx. It's user-friendly and allows for data transformations, making tasks easy to understand. 

My main use case for Databricks involves the pipelines and ETL processes that we are implementing. Following the Medallion architecture with Gold, Silver, and Bronze layers, we filter the data, perform transformations, and integrate AI. Databricks has made this process significantly easier.

I worked for an airline company where they experienced substantial delays in data processing. When a passenger booked a ticket, it took 20 to 25 minutes for the transaction to reflect in the system. Using Databricks, we compressed that time from 10 to 6 minutes initially and eventually reduced it to just a few seconds. After setting up all the pipelines and leveraging Databricks features to enhance and accelerate the process, this project became truly impactful and time-based, resulting in reduced processing time and ultimately increased profit for the airline company.

The best features Databricks offers are Unity Catalog, Databricks Workflow, Databricks AI, Agentic AI, and the automated pipelines that utilize AI. The AI models are very easy to create and deploy in just a few seconds. These are helpful and user-friendly tools.

I find myself using Unity Catalog most frequently because it provides a unified governance solution for all data and AI needs on Databricks, offering centralized access control, auditing, lineage, and data discovery capabilities across the platform. The main features include access control, security compliance standard models, built-in auditing, and lineage tracking. Most of my projects have involved integrating Unity Catalog into systems and providing overall security, including a migration project to transition to Unity Catalog.

The platform's unified data intelligence capabilities allow teams to analyze, manage, and activate data at scale, leading to faster time to insights, cleaner data pipelines, and significant savings on infrastructure and engineering efforts. Databricks eliminates data silos, accelerates the time to insight, empowers all data personnel, and provides built-in governance and security. It also supports AI and ML, which is an added advantage in today's AI-driven field.

Databricks already provides monthly updates and continuously works on delivering new features while enhancing existing ones. However, the platform could become easier to use. While instruction-led workshops are available, offering more free instructional workshops would allow a wider audience to access and learn about Databricks. Additionally, providing use cases would help beginners gain more knowledge and hands-on experience.

Regarding my experience, I was initially unfamiliar with the platform and had to conduct research and learn through various videos. I did find some instruction-led classes, but several of those required payment. The platform should provide more free resources to enable a broader audience to access and learn about Databricks. The platform itself is user-friendly and easy to use without complex issues, so I believe it does not need improvement in its core functionality. Rather, supporting aspects can be enhanced.

I have been working as a data engineer for four years. Initially, I was a software engineer, but my career has progressed as a data engineer over this four-year period.

Definitely. As I mentioned regarding my airline project, it was impactful because the cost was reduced by 60 to 70 percent. The company was initially using Azure Blob storage, and in Databricks, the cluster and associated infrastructure were cheaper than other platforms. This reduction in both time and money resulted in real-time impact and significant cost savings.

For advice for others considering Databricks, it is important to start by understanding its place in the data ecosystem and how it fits into your specific needs. Key points to consider include familiarizing yourself with Databricks, learning the basics, starting with data engineering, and incorporating ETL processes. You can then dive deeper into Databricks features such as notebooks, clusters, and jobs. Achieving certification enhances your skills validation. For best practices, it is critical to optimize performance and minimize complexity while continuously learning to stay competitive in the data field. Following these steps will be very beneficial for anyone pursuing a career as a data engineer and Databricks engineer.

Databricks is a truly essential platform for data engineering needs, and I recommend it to anyone looking to advance in the data engineering field. It is a very important platform and tool for every data engineer. I encourage everyone to learn and explore this product and to maximize its potential. I rate this product a 9 out of 10.