- Forward-looking enterprises are increasingly using AI, NLP and other Machine Learning (ML) technologies to enhance their operational workflows.
- However, even if enterprises have access to massive amount of data, processing this data is not easy. This is mostly because data is stored in silos and it’s not easy to unify it and process in a single platform.
- Security and data governance is also a major challenge when it comes to processing big data.
- Databricks offers a modern, unified platform to manage data, analytics and AI workloads using a lakehouse architecture that is highly flexible and scalable.
ABN AMRO’s use of Databricks in their digital transformation
ABN AMRO, one of the world’s leading banks, had set itself a mission of becoming a fully digital bank. However, it was unable to completely modernise its operations primarily because the company couldn’t get rid of its legacy IT infrastructure.
While the bank was using modern data warehouses, data was still stored in silos – across various departments and locations – making it difficult to access and use to garner insights.
Additionally, data management processes were becoming inefficient and the bank was struggling to reinvent itself and become completely digital. Eventually, the company decided to overhaul its entire data infrastructure and implement a Lakehouse Architecture on the Databricks Platform.
This proved to be a game-changer, making life simpler for 500+ data engineers, data science professionals and business analysts who worked at the bank. By migrating to the lakehouse architecture, business leaders at the bank were able to collaborate better and make data-driven decisions with ease.
The company designed a series of business intelligence and AI engines to enhance various processes including marketing automation, fraud detection, risk management, day-to-day operational processes and more.
Reliability and Governance of Databricks on Azure, AWS and Google Cloud
Data Lakes are often used because they are open, flexible and ideal for machine learning processes. Most cloud platforms including Azure, GCP and AWS offer the necessary support to build and manage data lakes. However, while using data lakes, reliability, data security and governance require the special attention of experts, taking up time and resources.
The Databricks Platform was built to solve this particular problem of reliability and governance. A lakehouse is nothing but an open architecture that combines the best features of both a data lake and a data warehouse. One of its biggest advantages is that storage is decoupled from compute, making the overall architecture more efficient.
Databricks as a Delta Lake for increased performance and reliability
One of the key products of Databricks is Delta Lake. This offers an open format storage layer for all types of data — unstructured, semi-structured and unstructured. This unifies data into a single platform, while also ensuring performance and reliability.
Data bricks’ built in SQL editor for better data virtualisation
Databricks SQL (DB SQL) offers a built-in SQL editor complete with dashboards and visualisation capabilities to quickly gather insights from data. The biggest advantage of Databricks is that you can use your preferred BI tool (Tableau, PowerBI, dbt, Fivetran, etc.) without having to move data to another data warehouse. It also ensures that data administration and governance is a breeze.
Databricks ML data processing lifecycle
Additionally, Databricks ML is designed to support data processes across the machine learning lifecycle, at scale. At ABN AMRO, this was a key requirement to ensure business processes were truly leveraging the latest technologies like ML to improve business operations. The company used machine learning algorithms for fraud detection, by automatically parsing thorough millions of transactions and this wouldn’t have been as seamless without a platform like Databricks ML.
The Need for a Single Data Platform that is Reliable and offers High-Performance
As organisations grow and generate high volumes of data, siloes are automatically created.
The data layer starts to become more complex and fragmented systems make it difficult to prototype and operationalise data-driven solutions. Often, BI and data teams use a variety of data engineering tools to try and reach all “data sources”, but that is not very efficient and adds unnecessary complexity.
Some of the key challenges organisations face when it comes to high-volume data are:
- Difficulty in scaling up their analytics and AI processes to keep pace with information growth
- Unable to use unstructured and semi-structured data to garner insights
- Lack of proper data governance and data federation practices
- Unable to build streaming apps to ensure real-time data can be used for decision-making
- Unable to empower the business user and deliver self-service analytics at scale
Key Benefits of Using Databricks
Today, organizations work with unstructured data, which expands the scope of intelligence that can be gathered. A unified data platform such as Databricks reduces the batch processing time and pulls together all kinds of data in one place. This enables scheduling, running, and debugging applications in production while also empowering business users with self-service capabilities for interactive data exploration and visualisation. Business users can create real-time, interactive dashboards to generate dynamic reports and connect to their preferred BI tool for further analysis.
Faster reactivity to business goals and market changes
By aligning data science and engineering with business goals, the platform facilitates faster innovation, speed to market, and swifter response to customer needs, thereby increasing customer delight.
As one of our data management experts at Merit puts it: “The Databricks Platform also improves access to data from multiple sources through virtualised storage without the need to create a separate data warehouse. It separates computing from storage and offers support for high-volume data and AI workloads. It also supports ACID (Atomicity, Consistency, Isolation, and Durability) transactions, which increases reliability and integrity of data operations.”
Flexible job scheduler
Databricks has a flexible job scheduler that allows a prototype to transition seamlessly to the production environment without much additional effort. Custom alerts enable monitoring the progress in real-time and new deployments can be automatically relaunched in case of a failure.
Highly secure and scalable
Additionally, the production environment in Databricks is highly secure and it can be used to build new clusters rapidly. These clusters can be scaled up or down as needed.
Databricks platform’s collaborative and integrated environment democratizes and streamlines data exploration, prototyping, and operationalisation of data-driven applications. It accelerates ETL by allowing anyone with authorisation to directly query the data using a simple interface.
The Databricks platform ensures data security and provides role-based access control to all components of the organisational data infrastructure, such as files, code, application deployments, clusters, caching, reports and dashboards.
At-rest and in-flight encryption using best-in-class standards such as SSL and keys stored in AWS Key Management System (KMS) further strengthens data security. Integrated Identity Management seamlessly integrates with enterprise identity providers using Active Directory and SAML 2.0. Strong data governance ensures monitoring and auditing of all actions taken at every data infrastructure level. Databricks is SOC 2 Type 1 certified and is HIPAA-compliant.
Merit Group’s expertise in Data Lakehouse Architecture
Our data engineers can help you with faster time-to-insights using the Databricks Platform, especially when one is looking for high-volume data and AI workloads at scale.
Get in touch with our data and business intelligence teams for strategic guidance on building the right data ecosystem, custom-designed for your business.
We’ll also help you choose the right cloud platform and data ecosystem based on your volume and/or types of data to be stored/processed.
If you’d like to learn more about our service offerings or speak to a data science expert, please contact us here: https://www.meritdata-tech.com/contact-us
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