This post is contributed by Burtch Works’ data science and data engineering recruiting team.
The data science and data engineering fields are continuously evolving to keep up with developments in artificial intelligence. Our Data Science and Data Engineering teams have identified some current hot tools that our clients are asking for in these respective fields.
Machine Learning Operations (MLOps) are the best processes for collaborative communication. Providing a workspace to fulfill the entirety of a data science project. New tools are becoming more used and common in the workplace. Here are some of the more common ones we are seeing our clients asking for candidates to have experience with.
Databricks is a cloud-based big data processing and machine learning solution that unifies data science and data engineering. It was recently added to Azure and integrates well with many applications, including the most commonly used programming languages such as Python, R, and SQL. One benefit is that it allows for a collaborative workspace on real-time coding in notebooks, which is helpful to have a more streamlined process.
A machine learning program, Dataiku is useful for conducting in-depth statistical analysis with an ability to create visual models. It is a fantastic tool for end-to-end data science with integration possible to other visualization tools (ex: Tableau). Dataiku is versatile with the ability to interface with many execution engines and databases.
Domino is a Platform-as-a-Service (PaaS) that provides modern cluster management with support for most data analysis languages. It features easy synchronization by updating the common platform. Also, it allows for quick scalability up to a 32-core machine.
An open-source tool, Apache Airflow’s primary purpose is to manage a system’s workflow. It is a dynamic and scalable platform that allows for easy management of workflows and pipelines by visualizing them as Directed Acyclic Graphs (DAGs). Airflow integrates well with third-party services like Microsoft Azure, AWS, and Google Cloud Platform (GCP).
Storage For Large Data
Besides leveraging an MLOps platform, many data engineers must use systems that allow for the storage of very large data sets. While “big data” as a term has come and gone over the past decade, the reality is that large data sets (transactional, streaming, etc.) Additionally, data is not all just numbers contained in structured tables, as has typically been the case in the past. Now there is unstructured data (text, images, video, and audio), and semi-structured data too. Managing these data types requires organizations adopting new platforms or customizing current tools.
Another rapidly growing tool, Snowflake, is continually requested by our clients. Snowflake is a streamlined cloud-based data warehousing allowing for the ability to store and compute data. The primary benefits of Snowflake include its shared data architecture and its highly scalable.
Apache Spark was created as a framework and library for data processing and storage. The platform has massive computing power to complete processing tasks and distribute data simultaneously. Spark can work with most major programming languages and processes quickly with its in-memory data engine.
An interface for Apache Spark, PySpark is a library that allows those coding in Python to use Apache Spark. The processing engine is used for in-memory computation, optimization, distributed processing, and more. PySpark is a great engine to learn if you’re already familiar with Python to create pipelines and conduct analysis.
Neo4j is a graph database that stores nodes and relationships, instead of tables. In today’s connected world, the connections between items are often as, or more, important than the items themselves. Using a traditional database with a rigid schema often requires many joins and cross-lookups to model the relationships, whereas a graph database allows problems involving many-to-many models the relationships with data that differs to be represented more easily without the restrictions of a pre-defined model.
Auto Machine Learning (AutoML)
Not everyone working with data has an advanced degree in data science or computer science. For those who are subject matter experts in a particular area of expertise, an auto machine learning tool (AutoML) may be helpful for them to build models with their data. Here are some examples of a few AutoML tools.
An auto machine learning tool for data scientists that builds models and suggests recommendations based on accuracy. The models created by DataRobot then allow the user to compare the options against each other. DataRobot is a great option for its usability, visualization, automation, and models.
An open-source platform, H20.ai’s autoML takes some of the most difficult workflows and makes them simple to understand. Its driverless AI applies the best data science practices to its models. H20.ai can create visualizations quickly and integrates well into existing workflows.
So which tool should you learn?
As you can probably guess, there is no one-size-fits-all answer.
The best thing you can do is to take a look at the job descriptions for the industries or companies where you think you might want to work and see what they’re looking for. Regardless of which tool you learn, if you only know one tool your options may be limited. The more tools you’re familiar with, the more adaptable you can be, and since this industry is constantly evolving, it’s important to keep learning new tools in order to keep your skills up-to-date with market demands.