On Aug 5, 2022, the CEO of Databricks announced Friday that his company has surpassed $1 billion in annualized revenue. This is more than double the $350 million in annualized revenue it reported just two years ago. This clearly rivals Snowflake’s growth and paves way for Databricks’ much-anticipated IPO in the near future.
Data is more expensive than oil. However, while data is an asset, lack of a unified data strategy can be a bottleneck, a huge cost to business and can lead to competitive disadvantages. According to some estimates in 2022, we generate a total of 2.5 quintillion bytes (2.5 e+9 GB) of data every single day. Most companies, especially large and mid-size companies have data silos that separate data engineering, analytics, BI, data science, and machine learning. The data strategy can be quite complex. It's common to see multiple data sources and duplication of data, not just in the same data store but also in multiple data sources within the same organization. Imagine a Fortune 500 that has acquired companies with different technology stacks and polyglot persistence.
The video showcases a cutting-edge Chat bot that is designed specifically for private enterprise data. This showcase highlights the possibility of a human-like intelligent solution that is platform-agnostic, customizable, and prioritizes data privacy with added role-based security. The model can incorporate the latest data, despite being trained on a small dataset. Learn more...
We live in a world in which we need real time actions, not just analysis. We need to be able to allow the AI based models to intelligently offer an experience that users are looking for in real time. In order for that to happen, traditional silos do not work. It’s too slow and complex. Databricks Lakehouse Platform solves that problem.
Before the advent of NoSQL databases, most data solutions were structured around RDBMSs (Relational Database Management System). They were great for structured data. Data warehouses used to import data from RDBMSs and were used to help in decision support and business intelligence applications. So, data warehouses were great for structured data. With the advent of phones, tables and IoT devices, modern enterprises must deal with enormous amounts of data: structured, semi-structured and unstructured. On top of that, they must deal with variable data, high velocity and volume. Data warehouses, even though adapted their architecture gradually, were not suited for such use cases. In some cases, they could be used to solve the problem, but at an elevated cost.
Given the limitations of data warehouses, architects envisioned a single data solution to gather company-wide data. Imagine a company with different workloads of data: structure, semi-structured and unstructured in different data sources. All that data would be now moved to a data lake, which is a single repository of data in multiple formats. However, some of the challenges were:
Artificial intelligence has made recent advances in processing unstructured data: audio, video, image and text. To solve this problem, gradually companies ended up with a bunch of systems: data warehouses, data lakes, and specialized systems (streaming, time-series, graph, and image databases). This makes the data strategy of a company highly inefficient, introduces delays, increases room for error and reduces competitiveness.
There has been an ever-increasing need for a flexible, high-performance data system that includes analytics, real-time monitoring, data science, and machine learning. The data lakehouse architecture was created after understanding the current and future needs of the enterprise. It’s an attempt to address the limitations of data lakes. A data lakehouse is a new, open architecture that not just combines the best elements of data lakes and data warehouses but also addresses more use cases and fixes the underlying challenges. Data lakehouses redesigns data warehouses for the modern world. It uses similar data structures and data management features directly on top of low-cost cloud storage in open formats.
Machine learning is a very innovative field and is very disruptive. However, ML lifecycle is still very new compared to SDLC (Software Development Lifecycle). MLOps (Machine Learning Operations) is to ML what DevOps (Development and IT operations) is to SDLC. Building models and creating pipelines that seamlessly deploy the models into production is quite complex. According to multiple estimates in 2022, more than 60% of models do not make it to production. ML requires working with unstructured data, but we still have the same versioning and governance needs for that data as we have for structured data.
Databricks supports all major ML platforms and technologies. Data scientists and engineers can use R and Python to access the data. MLflow, an open-source platform developed by Databricks, automates the end-to-end ML lifecycle and has become a leader in simplifying the process of standardizing MLOps and deploying the ML models to production. The ML lifecycle can be very complex depending on the underlying business problem we are trying to solve. We need to version different models along with related code, dependencies, visualizations and intermediate data. This is required to be able to track what’s the current state of the deployment, what exactly needs to be redeployed and where, and in order to rollback updated models in appropriate cases.
Data lakehouse makes machine learning and data science data-native, which enables us to perform everything from exploratory data analysis, model training and model serving exactly where the rest of the data is being managed. It has everything you need for a team to collaborate and is designed keeping in mind the different personas: data engineer, data scientist, ML engineer, business stakeholder and data governance officer.
In our recent keynotes delivered in five continents world-wide we have demonstrated how we can scale the current applications with high performance and at a fraction of the current cost using modern technologies and architecture. The main highlights of this keynote were:
In scalability best practices, the pros and cons of all major database architectures were discussed. The discussions included CAP Theorem, whitepapers on scalability, different types of NoSQL databases along with their use cases, polyglot persistence, scalability bottlenecks and how to avoid them. Finally, the best principles for an auto-scale architecture were discussed. Quite interestingly, the highly scalable architecture was also quite inexpensive to create as well as maintain.
At Cazton, we help Fortune 500, large, mid-size and start-up companies with Apache Spark and Databricks Lakehouse platform and MLflow development, consulting, recruiting services and hands-on training services. We have an added advantage of creating best practices after witnessing what works and what doesn’t work in the industry. Our team includes expert data engineers, data scientists, ML engineers, developers, consultants and architects as well as experts in related technologies. With an expert-led team like ours, we bring the benefit of our network to you. We save our clients a serious amount of effort on tasks they shouldn’t be doing by providing a streamlined strategy and updating it timely. We offer the following services:
Our team has great expertise in building multi-million-dollar enterprise web applications (with billions in revenue per year) for our Fortune 500 clients. Our team includes Microsoft awarded Most Valuable Professionals, Azure Insiders, Docker Insiders, ASP.NET Insiders, Web API Advisors, Cosmos DB Insiders as well as experts in other Microsoft as well as open-source technologies. Our team has mentored professionals worldwide by speaking at top conferences in multiple continents. With the frequent changes in operating systems, platforms, frameworks and browsers; technical folks have a lot to keep up with these days. They can’t specialize and be experts in every technology they work on. With an expert-led team like ours, we bring the benefit of our network to you. We save our clients a serious amount of effort on tasks they shouldn’t be doing by providing them with a streamlined strategy and updating it when we get more information. We work with Fortune 500, large, mid-size and startup companies and we learn what works and what doesn’t work. This incremental learning is passed on to our clients to make sure their projects are successful.
Cazton is composed of technical professionals with expertise gained all over the world and in all fields of the tech industry and we put this expertise to work for you. We serve all industries, including banking, finance, legal services, life sciences & healthcare, technology, media, and the public sector. Check out some of our services:
Cazton has expanded into a global company, servicing clients not only across the United States, but in Oslo, Norway; Stockholm, Sweden; London, England; Berlin, Germany; Frankfurt, Germany; Paris, France; Amsterdam, Netherlands; Brussels, Belgium; Rome, Italy; Sydney, Melbourne, Australia; Quebec City, Toronto Vancouver, Montreal, Ottawa, Calgary, Edmonton, Victoria, and Winnipeg as well. In the United States, we provide our consulting and training services across various cities like Austin, Dallas, Houston, New York, New Jersey, Irvine, Los Angeles, Denver, Boulder, Charlotte, Atlanta, Orlando, Miami, San Antonio, San Diego, San Francisco, San Jose, Stamford and others. Contact us today to learn more about what our experts can do for you.