Maneuvering through the world of big data is no easy feat. Data Scientists have the daunting task of setting up big data clusters (like Hadoop, Spark, and other Apache open-source projects) in a way that makes non-tech people dissociate into the void.
Databricks – a cloud-based, fully-managed, big data processing platform – is the hot new tool our data experts are using to make their lives easier and more productive.
In simple terms, Databricks makes managing data clusters easier by providing a user-friendly interface that can process huge amounts of data in a high-performing and scalable way.
Sai Kumar Enumula, a Senior Data Engineer at Tensure Consulting, likes Databricks because of the efficiency it provides.
“Databricks makes life so much easier. Not only can it support interactive clusters, it can also automate the setup and scalability of Spark multi-machine clusters, making my job run smoother and more effectively.”
Data comes in many different formats, so Databricks allows engineers to create code in any language they choose, making data manipulation and processing easier.
“As an example, I can use Scala for object-oriented support, or Python for JSON parsing,” says Sai. “Additionally, I can connect to many different data sources, which also supports streaming and graphical data for easy visualization.”
Notebooks are a type of interactive computing that allows engineers to write code, then visualize and share the results. Sounds peachy, right?
Unfortunately, many developers hate using Notebooks, because they are difficult to use, and can’t effectively integrate with other applications to visualize data in a useful way. Databricks Notebooks, on the other hand, challenges this common experience. In Databricks, engineers can easily create cells in different languages – easing integration with other apps – making visualization more effective.
Sai adds,
“Notebooks in Databricks are much easier to use, because I can create dynamic reports with multiple insights without having to use external reporting software tools like Tableau.”
Data can be deployed to either Azure or AWS, and can leverage the advantages of these cloud providers.
Sai continues,
“I can make Databricks integrate with multiple useful tools – like MLFlow, SageMaker, Data Factory, and more – making the data manipulation and visualization more dynamic and easier to process.”
Databricks has extensive documentation of its use-cases that are easily available in multiple languages for engineers to refer to. As Sai mentions, “this makes my job easier to get started on, making distributed analytics much easier to use.”
Put simply, Databricks makes life simpler for Data Engineers. Here at Tensure, we love maximizing these types of tools, so we can produce faster results with less headache.
If you’re interested in reading more about tools that make life easier, check out our Tech We Love Series article called 5 Tools that DevOps Experts Love in 2022.
