Top 10 Conventional Data + AI Tools and Why Databricks Might be the Next Big Thing

Introduction

Here’s a fact that might have been lost in all the hype built by the arrival of ChatGPT and other GenAI models. Before diving into Generative AI, organizations must ensure that they build the underlying data infrastructure and processes. The mandatory first step is to embrace and enable robust data analytics and AI strategies before taking the next leap.

While Generative AI is the future, many companies are yet to implement conventional AI solutions as part of their digital transformation initiatives. It’s here that the idea of data + AI platforms gains prominence.

In its 2023 State of Data + AI report, Databricks highlights the growing trend of organizations unifying AI and data analytics on the same platform. At every business level, experts need to combine data and AI to drive smarter business decisions.

In this blog, we shall look at the top 10 data + AI tools and shed light on why Databricks may turn out to be the “next big thing.”

All About Data + AI platforms

As compared to “conventional” data analytics platforms, data + AI platforms offer a host of capabilities, including:

• Integrating diverse enterprise data from different sources
• Improving AI algorithms to integrate data from multiple data sources – thus reducing bias
• Addressing data security and privacy challenges (often reported with Generative AI)
• Making it simple for data professionals to understand and consume data for accurate insights

An integrated data + AI platform is a prerequisite to implementing Generative AI. Be it any industry, organizations are utilizing enterprise data (from various sources) to feed into large language models (LLMs) to capably leverage Generative AI. High-quality data is proving to be the foundation for Generative AI models for extracting meaningful insights.

To that end, here’s a look at the top 10 data + AI platforms available in the market.

1. Databricks

As a cloud-powered platform, Databricks enables organizations to process and transform massive volumes of data for use cases like business intelligence and data warehousing. Among its many benefits, Databricks uses the open-source Apache Spark framework, which means zero vendor lock-in.

Besides, Databricks offers a host of advanced capabilities like the facilitation of serverless architecture and support for Kubernetes. With its native cloud support for data warehouses and lakes, it is highly suitable for AI and data analytics applications.

2. Snowflake

Snowflake is a unified SaaS platform designed for data warehousing, lakes, engineering, and application development. As a self-managed cloud service, Snowflake does not need any physical hardware or software installation.

On the flip side, Snowflake does not directly support all AI and machine learning use cases – rather, it depends on third-party applications. It’s also noteworthy that Snowflake reports performance issues when handling heavy data volumes.

3. SAS

Short for Statistical Analysis System, SAS is a privately owned company that has pioneered data analytics technology. As a leader in data analytics, SAS can easily transform data into intelligent insights. Some of the advantages of SAS include easy learning and debugging, tried and tested algorithms, and its ability to handle large databases.

Among its disadvantages, SAS is not an open-source tool and has limited graphic representation. Its AI capabilities are also limited when compared to other tools.

4. Apache Storm

Developed as an open-source platform by Apache Foundation, Apache Storm offers a host of capabilities, including:

• Real-time computation for workload management
• High processing speed (of up to 1 million 100-byte tuples per second)
• Horizontal scalability
• Fault tolerance

Among the limitations, Apache Storm is built for applications involving data stream processing. Contrarily, Databricks, for instance, is usable across use cases involving batch, interactive, and iterative processing.

5. TensorFlow

TensorFlow is a popular tool used for big data analytics. Designed as an open-source tool by Google, TensorFlow enables companies to analyze large datasets quickly and accurately. Additionally, this data analytics tool uses various data algorithms to identify data patterns and trends. With TensorFlow, companies can train AI models with high-quality data and extract valuable insights.

However, TensorFlow has its share of limitations, including:

• Lack of Windows support
• Supports GPUs only for NVIDIA and Python language
• Steeper learning curve and challenging to understand when compared to the likes of Databricks

6. Amazon Web Services (AWS)

As part of its AWS cloud offerings, Amazon offers Amazon S3 (for cloud storage) and Amazon Kinesis (for data analytics). Designed for real-time applications, Amazon Kinesis is a cloud-powered analytics tool with the following capabilities:

• Real-time processing of large gigabytes of data
• Real-time analytics on the collected data
• Easy integration with other AWS services like Amazon S3, DynamoDB, and RedShift

When compared with Databricks, Kinesis has a few limitations, including:

• Lack of beginner-friendly documentation
• Proves expensive to handle high data volumes
• File size limitation (only up to 10MB)

7. Azure HDInsights

HDInsights is a cloud-hosted open-source service for the distribution of various Hadoop components. Some of the advantages of this tool include:

• Ease of use, fast, and cost-effective service
• Suitable framework for Hadoop, Spark, R, Kafka, and more
• Highly scalable and productive
• End-to-end data protection and governance

One of the limitations of this tool is that it does not support Apache Storm.

8. Google BigQuery

Google’s BigQuery is a serverless data warehousing tool that can analyze petabytes of data. It supports querying using ANSI SQL commands. This tool also has in-built AI and machine learning capabilities, including:

• BigQuery machine learning and BigQuery BI
• Data insights with its NLP capability
• Data transfer from Amazon S3 and Teradata

However, as compared to Google BigQuery, Databricks is more suitable for data science projects that require integration with Apache Spark and MLflow. Databricks is also more flexible for coding in languages like Python, Scala, and R.

9. Apache Hadoop

Apache Hadoop is an open-source tool designed to store, process, and analyze big data. Here are some of its capabilities:

• Distributed processing of massive datasets across clusters
• Distributed storage and computation for Hadoop applications
• Parallel data processing instead of sequential processing
• Designed to scale from one server to thousands of connected servers

But it makes sense for organizations to migrate from Hadoop to Databricks. Here’s why:

• Simplified architecture using Lakehouse
• Centralized data security and governance
• Better productivity and business value
• Improved performance across data workloads

10. Splunk

Splunk is the pioneer in collecting and analyzing massive volumes of machine-generated data. It has a growing ecosystem of over 2,400 business partners and applications.

The platform serves multiple use cases, including cloud migration, IT, and application modernization. It is leveraged across industries like healthcare, aerospace, and finance.

Final Thoughts

In 2023 and beyond, Generative AI is emerging as the playground for the next big wave of innovation. However, companies must consider implementing a “conventional” data + AI platform to maximize their returns from Generative AI.

From all the tools above, Databricks proves to be a viable option for organizations because it enables scalability and cost-effectiveness, faster AI implementation, multi-cloud support, and more.

As a Consulting and SI partner for Databricks platform, Pratiti  Technologies is among the leading providers of data science and analytics solutions. Our experts can help you navigate the intricacies of successfully leveraging a data + AI platform. Contact us to learn more.

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