How Databricks Transformed Real-Time Data Processing for Advanced Analytics

Table of contents

• From databases to unified data lakehouses

• Why you need real-time data streaming for advanced analytics

• Challenges of building real-time analytics (and how Databricks came to solve them)

• Examples of user-facing real-time analytics

• Using Databricks for advanced analytics with NaNLABS

• Frequently asked questions about Databricks Unified Data Analytics Platform

From databases to unified data lakehouses

There was a time when all you needed was a relational database to store your business information. However, when businesses became connected over the Internet, this data architecture could no longer cope with the huge influx of diverse information.

At first, companies solved this issue by paying for more relational databases but this led to data silos—information was scattered and disconnected. This led to the creation of data warehouses.

Data warehouses were mostly built for structured information and reporting needs. However, the rise of big data caused warehouses to fall short. 

Companies need to store structured, semi-structured, and unstructured data to power ML models and AI programs—enter, data lakes. The first data lakes were built on-premise using Apache Hadoop, but you can now host them on the cloud.

Data lakes are substantially better for managing diverse data types for analytics, ML models, and AI features, but they lack consistency. This makes it hard for teams to process batch and streaming tasks. 

Databricks faced this problem and introduced a data lakehouse architecture in 2020. 

Such as with data lakes, you can store both raw and structured data in data lakehouses. Plus, in a lakehouse, you can also make ACID (atomicity, consistency, isolation, durability) transactions, support scheme enforcement, and make data more queryable (using Delta Lake, for example).

In short, lakehouses combine the flexibility of data lakes with the performance and reliability of data warehouses. Lakehouses also provide indexing, governance, in-place transformations, and real-time processing.

Data lakehouses and the Databricks Unified Data Analytics Platform

Databricks offers customers the ability to host lakehouses to simplify data management across teams. The Databricks Unified Data Analytics Platform essentially brings your data engineers and scientists together by allowing them to work simultaneously on a single platform. This enables your team to solve problems faster and process data efficiently.

Through the Databricks Unified Data Analytics Platform, you can prepare data, train ML models, build BI reports, and stream real-time analytics in a single pipeline. Also, since Databricks integrates with most AWS services, you can continue using the databases you already know—like Amazon RDS. 

Why you need real-time data streaming for advanced analytics

Imagine your kids are hungry and asking for a grilled cheese sandwich, but you’re out of bread. You text your partner and ask them to buy bread on their way home. However, they don’t see your message and get home without it. They head out again to go to the store. Your kids asked for a grilled cheese an hour ago, they’re now irritable. You think: Only if I had immediate access to bread when they asked...

Something similar happens when you build advanced analytics. Your users want to know how their product is performing now (they’re hungry) but that’s impossible without real-time data ingestion and streaming processes (bread). 

Real-time data streaming enables you to process and analyze data as soon as it’s created. This way, you can provide users with timely insights and allow them to make faster data-driven decisions through analytics.

The Databricks Unified Data Analytics Platform allows you to set parameters for batching and streaming, and clean data at a massive scale. By unifying your data for analytics, you can simplify big data management and innovate faster. 

So, you need to set up real-time data streaming processes for advanced analytics to: 

Enable users to make faster decisions

When you stream data in real-time or near-real-time, you get to offer your customers answers in the same capacity. This is crucial to stay competitive as you can review data to make decisions faster.

Leverage ML models

You need ML models to develop advanced analytics. These models often need real-time data ingestion to make accurate and timely predictions. Also, streaming data in real-time enables ML models to stay dynamic and process new input as the system runs.

Support predictive analysis

While you use your historical data to forecast future scenarios, real-time data streaming guarantees that those predictions remain accurate as conditions change. Think of food delivery apps. You can predict how long it’ll take for a user to get their order based on historical records and the current demand. However, if a restaurant takes twice as long to create an order, you need real-time data streaming to adjust the expected delivery time.

Conduct real-time anomaly detection

If there’s code, there’ll eventually be bugs or security threats. This isn’t necessarily an issue, how you detect unusual behavior before it affects your users’ experience is. Real-time data streaming allows you to get alerted in case of malfunctions and prevent any security, reputational, or satisfaction damage.

Monitor and optimize IoT devices

Most of your data ingestion is likely coming from IoT devices. These are filled with sensors that generate huge amounts of data that you have to process, analyze, and use. You need to have the right processes in place to stream and analyze this data in real time and offer advanced analytics.

Become more scalable

Setting up real-time batching and streaming processes gives you a sturdy base to scale your business as your data volumes grow. Setting up efficient batching and streaming workflows ensures systems have a scalable infrastructure that can process data efficiently without bottlenecks in the future.

Challenges of building real-time analytics (and how Databricks came to solve them)

To offer advanced analytics affected by real-time data, you need to identify and connect your data sources and set up data streaming technologies. This becomes challenging when your data isn’t reliable, your queries perform poorly, or you lack governance.

Let’s explore each of these challenges in detail:

Data reliability

Your data needs to be accurate to provide customers with valuable analytics. That’s why your ingestion and transforming processes must be properly set up. If you constantly need to reprocess or fix missing or corrupted data, you’ll slow down the system and limit its ability to offer real-time answers.

Poor quality data can also affect its accuracy and the system’s pipelines, leaving you with skewed and inaccurate responses. Another important factor that affects reliability is inefficient merging processes. Real-time streaming processes need to merge with historically batched data. If these can’t be combined, you’ll also present customers with inaccurate information. 

Traditional data lakes require you to process missing or corrupted data almost continuously. This process is quite challenging if the writing job fails because your engineers need to fix the corrupted data or complete the missing data by hand.

Databricks solves this issue through Delta Lake. For instance, you can make your data lakehouse transactional and atomic. This way every single job either is complete or fails on its own. Its success or failure doesn’t affect the rest of the lake and makes it easier to keep it clean.

You can also use Delta Lake to maintain data consistent and isolated during streaming and batching. This way everyone can see the same information, even if someone is making changes to the table in real time. 

Query performance

Performance is one of the most common data engineering challenges. When building real-time advanced analytics, you need to present your users with sub-zero loading queries. If your SQL query takes too long to load, you risk upsetting your customers. 

Some things that can affect your query performance include having multiple small files instead of a large one organized for analytics. Having everything in storage can also affect your query response time. Plus, poor metadata, indexing, or deleting processes can create bottlenecks that affect latency. 

You can overcome these challenges with Databricks. It allows you to retrieve data from your cache memory without having to make disk calls every time, accelerating response time. Also, via Delta Lake, you can also compact small files into a large one with indices and partitions, and optimized for analytics. 

Governance

Following data compliance and security regulations is key when handling business information. While data lakes can keep your data secure, fully adhering to regulations like GDPR or CCPA becomes harder.

According to Databricks: “Deleting or updating data in a regular Parquet Data Lake is compute-intensive and sometimes near impossible. All the files that pertain to the personal data being requested must be identified, ingested, filtered, written out as new files, and the original ones deleted.” This mishap complicates data management. Also, since some security regulations allow the user to ask companies to delete their data at any time, it puts you at risk of facing potential fines. 

However, the fact that it’s difficult to delete data puts you at more risk than being incompliant. Hosting unnecessary information could also affect your query performance due to system overhead. By adopting Databricks, you can keep your records clean, secure, and updated.

Examples of user-facing real-time analytics

When we talk about real-time analytics we’re not only referencing graphs and dashboards. You can find real-time analytics in most industries, here are some examples:

1. E-commerce. Every time someone shops online, they leave a trail of data. You can use this information to present customers with timely and personalized offers and recommendations. For instance, a user is shopping for a white plain tee and gets prompted with similar recommendations on a sidebar.

2. Social media. Real-time analytics allow social media tools like TikTok or X (formerly Twitter) to show what’s trending, give creators an overview of their engagement, or present users with timely ads. These all show different results based on real-time interactions and users’ behavior.

3. Delivery apps. Real-time analytics let you show the customer how long it will take for them to get their order based on the current demand and riders' capacity. For instance, you can access historical records to see the average preparation time for a certain restaurant and then match that with current demand. This estimation can change based on real-time events.

4. Banking and investments. Use real-time analytics to show users their account balance, how long it’ll take for a transfer to arrive, or how their investments are performing.

5. Car sharing and transportation. Through real-time analytics, users can see when their lift is coming to pick them up, how car-sharing prices change based on demand, or how long it will take for the bus to arrive at the stop.

Using Databricks for advanced analytics with NaNLABS

Databricks started out as a notebook tool for Apache Spark. However, it’s grown to become a lot more than that, and understanding Databricks can be a tough job.

If you find yourself juggling between tasks and trying to optimize your data lake for advanced analytics without succeeding, we can help out. At NaNLABS, we’re Databricks-certified partners and have over 11 years of experience lighting the load for companies like yours.

For example, earlier this year we collaborated with TeraWatt Infrastructure, an electric vehicles (EV) charging solutions company for medium to heavy-duty transports and fleets. For this project, we created a Charge Management System (CMS) to help fleet managers schedule EV charger usage. This CMS also supports reservations for pre-planning charge sessions at shared charging sites.

Since TeraWatt Infrastructure’s company streams data from smart devices, we implemented Databricks to process large data volumes and support real-time and batch data pipelines. Before Databricks, the client faced scalability, collaboration, and data quality issues. It also struggled to integrate diverse data sources.

Databricks allowed our team to combine data engineering, machine learning, and analytics capabilities in its Unified Data Analytics Platform. We also customized Databricks configurations to meet TeraWatt’s specific needs. These included defining custom transformation logic, optimizing Spark Executor resource usage, and creating standardized job templates for CI/CD integration.

This led to reduced data processing times by enabling real-time and near-real-time insights. Plus, thanks to Delta Lake, we managed to increase data accuracy and reliability, while relying on auto-scaling capabilities to keep the infrastructure costs low.

Are you facing similar issues with your data architecture? Let the NaNLABS team take the lead.

Get in touch

Editorial contributions: Camila Mirabal

Frequently asked questions about Databricks Unified Data Analytics Platform

What is Apache Spark?

Apache Spark is an open-source data analytics framework for managing big data. It processes huge amounts of data simultaneously across computers.

You can use Apache Spark for batch processing, developing AI features and ML models, or powering real-time advanced analytics. Use it directly or through Databricks for data management. 

What is Databricks Unified Data Analytics Platform? 

Databricks Unified Data Analytics Platform is an online service that allows data scientists and engineers to manage big data for advanced analytics in a single place. This makes teams collaborate more efficiently and get answers faster.