Databricks is a cloud-based tool used to engineer data to process and transform large amounts of data and explore the data using machine learning models.
This next-level technology is crucial in data analysis, facilitating value extraction from data.
Databricks utilizes AI’s flexibility, cost-effectiveness, and cloud storage. It has additional tools to maximize the productivity and security needed to ensure business growth.
Powered by Delta Lake, Databricks uses the lakehouse architecture (data warehouses and data lakes) to provide a single platform capable of collaborating and analyzing all my data and AI workloads.
Delta Lake is a storage layer that runs on top of the data lake file storage. Its format and the compute layer simplify building big data pipelines, increasing the efficiency of these pipelines. It’s compatible with Apache Spark APIs, for example, the AWS S3, Azure Data Lake Storage, or HDFS.
To store data, it uses versioned Apache Parquet™ and also holds a transaction log. This ensures keeping track of all commits made to provide expanded capabilities like ACID transactions, scalable metadata handling, data versioning, unifying streaming and batch data processing, and audit history.
Access to the data is made possible using the open Spark APIs, any of the different connectors, or a Parquet reader.
Databricks is known for its multiple advantages for the users, and those continue to be selling points for prospective buyers. Some of those advantages include:
It’s integrated with the cloud providers’ security, compute, storage, analytics, and AI services, unifying all my data and AI workloads.
Furthermore, Delta Lake ensures reliability, performance, and lifecycle management of data lakes.
It accelerates the velocity of high-quality data getting into my data lake, hence the rate at which teams can leverage data with a secure and scalable cloud service.
Data lakes often have issues with data quality, stemming from the absence of control over ingested data. Delta Lake has an added storage layer to manage data quality. This ensures that the data lakes contain only high-quality data.
Data is stored in the open Apache Parquet format, which allows the data to be read by any compatible reader.
As the business evolves, I can change records and exceed the Lambda architecture. This is made possible by unified streaming and batch using the same engine, APIs, and code.
Many data pipelines can read and write data concurrently to a data lake. ACID Transactions have serializability, which is the strongest level of isolation, guaranteeing my data integrity.
Delta Lake has DML APIs to merge, update and delete datasets. This capability allows me to comply with GDPR/CCPA and change data capture easily.
I can specify my data lake schema and enforce it, ensuring the data types are correct, and required columns are present, preventing data corruption brought by bad data.
Massive data is always changing. You can switch to a table schema that can be applied automatically, eliminating all the work that comes with DDL.
The data snapshots allow developers to access and revert to earlier versions of data to audit data changes, roll back bad updates, or even reproduce experiments.
On Delta Lake, the metadata is treated the same as data, complementing petabyte-scale tables with endless partitions and files.
All the data is stored in Apache Parquet format, allowing Delta Lake to leverage the efficient compression and encoding schemes originally found on Parquet.
A table in Delta Lake acts as both a batch table and a streaming source and sink. The streaming data ingest, batch historic backfill, and interactive queries can work optimally.
All transaction logs on Delta Lake are recorded in detail, including any changes made to the data. This is an added advantage for compliance, audits, or reproduction.
As a developer, I can use Delta Lake with the data pipelines I already have, with slight changes due to its full compatibility with Spark, which is most commonly used.
It has native connectors that easily ingest data into Delta Lake from all my applications, databases, and file storage with added speed and reliability.
Databricks have its downsides, the same as other business intelligence solutions. Some cons include:
Databricks is tailor-made for multiple business applications and has been specifically optimized for the cloud. It’s capable of running on AWS, Microsoft Azure, and Alibaba cloud, ensuring the ability to support customers worldwide.
For instance, businesses involved in providing financial services, advertising, public sectors, enterprise technology software, telecommunications, energy and utilities, healthcare, industrial sectors, media, entertainment, and internet technology.
With data skipping, it is possible to use statistics on data files to prune files very quickly during query processing using the ZOrder methodology.
The transparent caching fastens through automatic caching of data to a node’s local storage.
It has more efficient decoding made possible by boosting the CPU efficiency when importing data from familiar sources like database tables and flat files.
Databricks provides an interface to spin up an Azure cluster, interact with the cluster, and create notebooks for ETL, analytics, processing graphs, and machine learning. This enables me to easily share the notebooks with coworkers, save notebooks like scheduled jobs, and comment on cells in notebooks.
Additionally, Databricks can terminate my cluster after some time not in use or time-out, saving up on operation costs.
The Databricks notebook interface makes it possible to code in multiple languages in the same notebook. Apart from Spark SQL, other languages are Java, Scala, Python, R, and standard SQL.
Quick access to typically hard-to-build data scenarios is an added advantage. Less infrastructure overhead cost allows me to focus resources on business value.
The time used to market by processing raw data in extensive data infrastructure is reduced significantly. With the multiple different data combinations, teams can work efficiently with little or no engineering knowledge.
The platform is easy to learn, and Databricks provides excellent support and training.
Indeed, Databricks has become very useful in many business processes, especially in business intelligence, where data matters.
You can think of Databricks as the plumbing for your data infrastructure.
DashboardFox is the presentation layer to communicate the data securely to the stakeholders who need access to it.
While DashboardFox has all the data-level security, interactive dashboards, scheduled emails, charts, and visualization options that you expect from an enterprise BI tool, DashboardFox doesn’t incorporate any ETL and data storage capabilities that come with Databricks. The two products complement each other perfectly as part of a complete BI stack.
A few key differentiators of DashboardFox (as compared to our more complicated, expensive business intelligence competitors):
Cost. DashboardFox is a one-time fee, not a subscription charge like many BI tools these days. Pay once, use for life, and that initial fee, in many cases, is much less than an annual subscription for some tools.
Self-Hosted. You control where you install DashboardFox and your data (i.e., using Databricks). There’s no need to copy data into a 3rd party vendor cloud to be used by a Cloud BI tool. And because you host DashboardFox, you control the security, access, branding, and updates.
Priority Support. Included with DashboardFox is one year of Priority Support (and you can optionally renew support afterward). Our dedicated team of experts is at your beck and call whenever you experience issues using DashboardFox. But Priority Support is more than just technical support; it’s implementation assistance. We help make sure you’re successful with the tool, not just successful using the tool.
Reach out to us now to see what else DashboardFox can bring to the table.
Better yet, book a live demo session with us today.