Dataiku

What differentiates Dataiku from other similar products?

Your large data pipelines are made more manageable and readable with Dataiku. There are many built-in recipes that can be used to help you make your pipeline modular. It is unquestionably among the best tools for your systems.

What is your favorite aspect of this product?

I can work with unstructured data, which has a variety of schema records, as well as structured data, which has a standard schema, thanks to Dataiku DSS. Given that it is comparable to our SQL tables and customised SQL queries, it helps with dataset manipulation on a unique level. When we perform transformations on our datasets, it supports data files that are present in our application servers and Hadoop clusters and provides DSS recipes. With the Pandas library, Pig scripts, and Hive queries included, it can also run our Python scripts.

What do you dislike most about this product?

You can't always accomplish your goals with the assistance of recipes. Because of this, the pipeline must be entirely programmed in, let's say, a Python recipe, which again reduces its modularity. Less documentation is also accessible.

What recommendations would you give to someone considering this product?

It is kind of needed tool for dataops and data transformation into various dimensions. It is the best tool for cleaning and transforming data in accordance with various business needs.

What differentiates Dataiku from other similar products?

Dataiku can be used for a variety of use cases, including machine learning, data visualization, and data engineering. Dataiku offers collaboration tools that enable multiple users to work on the same project simultaneously.

What is your favorite aspect of this product?

Dataiku integrates with a wide range of third-party tools, including databases, cloud storage providers, and machine learning frameworks.

What do you dislike most about this product?

While Dataiku is versatile, it may not have all the features necessary for certain use cases, such as advanced natural language processing or computer vision applications.

What recommendations would you give to someone considering this product?

Dataiku's intuitive interface and drag-and-drop tools make it easy to use for users of all skill levels.

What differentiates Dataiku from other similar products?

The flows create it simple to see my data flow, rather than having multiple files everywhere. It saves me loads of your time once cleaning data and mixing completely different languages and technologies like SQL, Python and R within the same project. The plugin feature conjointly offers me a lot of flexibility. The fact that you simply will insert new data to your database from DSS, automate flows insert checks, and so forth makes the whole method very intuitive and saves a lot of time.

What is your favorite aspect of this product?

Dataiku DSS allows me to figure with each structured data, which features a normal schema, & unstructured data, which has various schema records. It aids with dataset manipulation at a definite level as it’s analogous to our SQL tables and custom SQL query. It may also execute our Python scripts with the inclusion of Pandas library, Pig scripts, and Hive queries. Dataiku DSS has many beneficial functions. That is very helpful to different organizations. It is so promising application.

What do you dislike most about this product?

Some minor bugs exist and a few options are still needed. I have faced bugs when group action with Snowflake, however no major issues. Since it’s a brand new tool, we often face problems because of missing options, however, the support team is usually willing to assist and add those features to their development pipeline.

What recommendations would you give to someone considering this product?

I take advantage of it for just about everything at work now, from recommender systems to forecasting. It saves me an enormous quantity of time.