29 Jul Overview of Data Bricks
Today, businesses rely on data to meet their goals and make important decisions.
Companies deal with large volumes of data, but this data is difficult to understand when it lives in separate silos. Modern data platforms solve this problem by unifying data and providing a range of analytical tools, making it easy for businesses to derive insights from their data.
We’re going to explore how Databricks works, and how it helps businesses get the most out of their data.
What is Databricks?
Databricks is essentially a cloud based engineering platform widely used to process and transform large quantities of data. It was created by the developers of Apache Spark and has broad capabilities across data engineering, data analytics and data science.
Rather than housing your data across multiple platforms, Databricks combines all of your important data and analytics onto a single platform, allowing data scientists, data analysts and data engineers to manage and organise your data to uncover deeper insights.
Databricks helps businesses find the answers to many of their data-driven questions, allowing them to meet their objectives and continue to grow and evolve.
The Databricks Lakehouse – “Delta Lake”
Databricks uses a unique data lakehouse architecture, which combines the flexibility and cost efficient capabilities of data lakes with the data governance and ACID capabilities of data warehouses to enable a fast, scalable and reliable data platform.
The lakehouse architecture:
- Is easy to use, cost effective and collaborative
- Lives in the cloud, making it scalable
- Simplifies and unifies your data by removing silos
- Has high levels of governance and security
What problem does Databricks solve?
Data is the key to unlocking many great insights. However, to find these insights, you need a good set of tools.
Databricks simplifies the process of analysing and organising extremely large volumes of data, allowing businesses to cost effectively analyse this data.
How does Databricks simplify the management of data?
Databricks simplifies data management by providing:
- Interactive workspaces for teams, allowing them to collaborate and explore their data.
- Fully managed Spark clusters, eliminating the need to self-manage clusters.
- A production pipeline scheduler, aiding the movement of data.
- A platform to use all of the Spark-based applications
Who should use Databricks?
Databricks is a useful platform for data scientists, data analysts and data engineers who deal with big data.
For data scientists and engineers, there is a data science workspace that enables users to manage computational infrastructure and data science experiments via notebooks.
For machine learning, there is an integrated end to end managed services environment for experiment tracking, model training, feature development and management.
How is Databricks being used?
Databricks is used across an array of different data intensive industries, including:
- Media and entertainment
Databricks has also been used in many different use cases. Examples include:
- Applying advanced analytics for machine learning and graph processing at scale
- Using deep learning for harnessing the power of unstructured data for AI, image interpretation, automatic translation, natural language processing, and more
- Proactively detecting security threats with data science and AI
- Analysing high-velocity sensor and time-series IoT data in real-time
- Making GDPR data subject requests easy to execute
What are some of the key features of Databricks?
Databricks has efficient data processing capabilities
Databricks provides engineers with many options to control how data is processed using a a wide variety of clustering configuration options. Clusters can be easily launched with hundreds of machines, controlled by policies and limits to manage infrastructure resources.
Databricks can be used across a range of cloud storage services
Because Databricks is a cloud-optimised platform, it integrates with a wide range of cloud storage services including: AWS, Alibaba cloud, Microsoft Azure and Google Cloud Platform. As such, Databricks is able to support most businesses across many industries, making it an effective data platform tool of choice.
Databricks integrated to machine learning models
Models can be trained and tested using the experiment tracking features to understand how models are performing. There are also many integrations to other ML frameworks and open source libraries giving data scientists flexibility and choice.
Databricks provides complete history for compliance and auditability
Databricks provides sophisticated features for storing and analysing historical data, using time travel functions to provide a point in time analysis of data. Combined with a SQL analytics interface, it is possible to create compelling visualisations and dashboards via powerful lakehouse querying features.
Databricks allows users to collaborate using notebooks
Databricks has a notebook interface where users are able to code in multiple languages, all in the one place. The languages supported include Spark SQL, Standard SQL, R, Java, Scala and Python. Built-in commenting allows users to exchange ideas and updates with each other.
Easy to use and learn
Databricks is easy to use, and has excellent training and support. This means that a wide range of data-centric employees and stakeholders can use it, increasing the likelihood of unearthing valuable insights.
Today, businesses are creating and using more data than ever before.
Modern data platforms, like Databricks, are streamlining the process of sorting, sharing and analysing massive volumes of data, allowing businesses to unlock deeper insights.
Do you want to stay ahead of the competition? Chat to us today to unlock the full potential of your data.