Data Engineering & Data Science

We are excited to introduce Databricks Assistant Autocomplete now in Public Preview. This feature brings the AI-powered assistant to you in real-time, providing... View the full article

Automation has revolutionized storing, managing, transferring, and analyzing data by quickly capturing and transferring large amounts of information between two platforms. Webhooks facilitate workflow automation by helping you connect applications in near real-time when a specific event occurs. It automatically triggers the data transfer action and can notify the destination about events or updates. However, […]View the full article

We are thrilled to announce an exciting new feature on the Databricks Marketplace that simplifies the process of setting up private exchanges for... View the full article

In the semiconductor industry, research and development tasks, manufacturing processes, and enterprise planning systems produce an array of data artifacts that can be fused to create an intelligent semiconductor enterprise. Through intelligent data use, an intelligent semiconductor enterprise accelerates time to market, increases manufacturing yield, and enhances product reliability. View the full article

With RudderStack Profiles Cohorts and Activations you can bring business teams closer to the data than ever before without comprising control.View the full article

Databricks is pleased to announce we are ranked #2 in the inaugural annual Glassdoor Award List of Best-Led Companies in 2024 ! At... View the full article

RudderStack Profiles enables every data team to power their business with reliable, complete customer profiles. In this blog, we show you how. View the full article

Successfully building GenAI applications means going beyond just leveraging the latest cutting-edge models. It requires the development of compound AI systems that integrate... View the full article

In the fast-paced landscape of data science and engineering, integrating Artificial Intelligence (AI) has become integral for enhancing productivity. We’ve seen many tools... View the full article

You can’t afford not to solve identity resolution – because when you do the value of every customer data initiative goes up, and the complexity goes down.View the full article

We recently introduced DBRX : an open, state-of-the-art, general-purpose LLM. DBRX was trained, fine-tuned, and evaluated using Mosaic AI Training, scaling training to... View the full article

How we reached 79.9% on the Spider dev dataset with Llama3 8B through savvy prompting and fine-tuning on Databricks. View the full article

AWS data engineering involves designing and implementing data solutions on the Amazon Web Services (AWS) platform. For those aspiring to become AWS data engineers, cracking the interview is somehow difficult. Don’t worry, we’re here to help you! In this blog, we present a comprehensive collection of top AWS data engineer interview questions for you. These questions have been carefully selected to cover a wide range of topics and concepts that are relevant to the AWS Data Engineer role. Understanding the concepts behind these questions would help you to successfully go through the interview. If you are planning to become AWS Data Engineer, I would recommend you to pass AWS…

The annual Data Team Awards highlight how diverse enterprise data teams are tackling some of the most prevalent and complex issues facing the... View the full article

You can build a customer 360 using SQL + dbt, but you’ll face significant challenges. Here are the benefits of declarative data modeling for customer 360.View the full article

Last year, we launched foundation model support in Databricks Model Serving to enable enterprises to build secure and custom GenAI apps on a... View the full article

In December, we announced a new suite of tools to get Generative AI applications to production using Retrieval Augmented Generation (RAG). Since then... View the full article

The Data Team Awards celebrates enterprise data teams' essential role in helping businesses across sectors face their most pressing challenges. With more than... View the full article

Introduction Organizations aiming to become AI and data-driven often need to provide their internal teams with high-quality and trusted data products . Building... View the full article

Data, analytics and AI governance is perhaps the most important yet challenging aspect of any data and AI democratization effort. For your data... View the full article

PostgreSQL, also known as Postgres, is an advanced object-relational database management system (ORDBMS) used for data storage, retrieval, and management. It is available on the Azure platform in a PaaS model (Platform as a Service) through the Azure Database for PostgreSQL service. Azure Postgres automates several tasks related to relational databases. However, it has low […]View the full article

Most businesses face a significant challenge in efficiently managing and extracting insights from disparate data. Azure Postgres offers a robust storage solution but needs built-in tools for performing complex analytics tasks, like building machine learning models. This is where Databricks comes in. Databricks is a comprehensive platform that offers scalability, advanced data processing tools and […]View the full article

The quality of your data analysis and the insights derived directly depends on the quality of the data you feed. This is why data cleaning is crucial in ensuring your datasets are accurate, consistent, and reliable for further analysis. Python, a versatile programming language, has many tools with various functionalities to streamline and optimize this […]View the full article

Many organizations today heavily rely on data to make business-related decisions. Data is an invaluable asset that helps you substantiate your convictions with evidence and facilitates stakeholder buy-in. However, ensuring your data is of high quality is paramount as it directly correlates to the accuracy of the desired results. Implementing data quality management techniques can […]View the full article

While AWS RDS Oracle offers a robust relational database solution over the cloud, Databricks simplifies big data processing with features such as automated scheduling and optimized Spark clusters. Integrating data from AWS RDS Oracle to Databricks enables you to handle large volumes of data within a collaborative workspace to derive actionable insights in real-time. This […]View the full article

Cloud solutions like AWS RDS for Oracle offer improved accessibility and robust security features. However, as data volumes grow, analyzing data on the AWS RDS Oracle database through multiple SQL queries can lead to inconsistency and performance degradation. If your organization is considering migrating large datasets to a new platform, it’s essential to look into […]View the full article

Most organizations today practice a data-driven culture, emphasizing the importance of evidence-based decisions. You can also utilize the data available about your organization to perform various analyses and make data-informed decisions, contributing towards sustainable business growth. However, to get the most out of your data, you should ensure your datasets are free of anomalies, missing […]View the full article

If your organization is data-driven, it is important to understand your data’s origin, movement, and transformation. This imparts transparency within your organization, ensures data integrity, and enables informed decision-making. You can use data lineage for this. If you use traditional methods of tracking data lineage, you will be required to create manual documentation, which is […]View the full article

The true value of data doesn’t lie in the vast amounts of data you generate from different sources but rather in the insights and intelligence it provides. Data intelligence solves the challenge of producing overwhelming amounts of data. It extracts meaningful insights from this huge data inundation. Organizations need data intelligence to analyze patterns and […]View the full article

In today’s digital era, businesses continually look for ways to manage their data assets. Azure Database for MySQL is a robust storage solution that manages relational data. However, as your business grows and data becomes more complex, managing and analyzing it becomes more challenging. This is where Snowflake comes in. Snowflake enables you to analyze […]View the full article

Moving generative AI applications from the proof of concept stage into production requires control, reliability and data governance. Organizations are turning to open... View the full article

In the fast-paced world of sports, where every second and every play can make a difference, the need for advanced analytics and real-time... View the full article

The generative AI revolution is transforming the way that teams work, and Databricks Assistant leverages the best of these advancements. It allows you... View the full article

The Databricks Data Intelligence Platform offers unparalleled flexibility, allowing users to access nearly instant, horizontally scalable compute resources. This ease of creation can... View the full article

The modern data stack is designed to address the difficulties with data collection, storage, and analysis as the volume and complexity of data... View the full article

A good benchmark is one that clearly shows which models are better and which are worse. The Databricks Mosaic Research team is dedicated... View the full article

We are excited to announce that Databricks on AWS GovCloud is now in public preview and that we recently earned our first FedRAMP®... View the full article

We are proud to announce that Forrester has recognized Databricks as a Leader with the highest scores in both current offering and strategy... View the full article

In today’s data-driven era, you have more raw data than ever before. However, to leverage the power of big data, you need to convert raw data into valuable insights for informed decision-making. When it comes to preparing data for analysis, you will always come across the terms “data wrangling” and “ETL.” While they may sound […]View the full article

Almost all companies today are “data rich.” They have access to exponentially more data than ever before. But they are still information poor, struggling to make sense of it all. One of the main reasons for this is disconnected data silos, acting as barriers that prevent a 360-degree view of their business. Data integration is […]View the full article

Organizations use ETL (Extract, Transform, and Load) to obtain quality data for expediting decision-making. But, the myriad of available ETL tools makes it challenging for organizations to evaluate and embrace the right tool. Today, ETL tools are divided into various types, making it even more difficult for companies to find the right fit. In this […]View the full article

Amazon Redshift is a serverless, fully managed leading data warehouse in the market, and many organizations are migrating their legacy data to Redshift for better analytics. In this blog, we will discuss the best Redshift ETL tools that you can use to load data into Redshift. 8 Best Redshift ETL Tools Let’s have a detailed […]View the full article

Today, companies have access to a broad spectrum of big data gathered from various sources. These sources include web crawlers, sensors, server logs, marketing tools, spreadsheets, and APIs. To gain a competitive advantage in the business, it is crucial to gain proficiency in using data to improve business operations. However, the information from different sources […]View the full article

According to a research report* by MarketsandMarkets, the data integration market is expected to grow from USD 11.6 Billion in 2021 to USD 19.6 Billion by 2026. This implies the huge potential of data integration and the two approaches to data management– ETL and ELT. However, in the battle of ETL vs ELT, choosing one over […]View the full article

It is common for people to get confused about the differences between data integration and data migration. While these processes are related, they serve different purposes and involve different approaches. Understanding the differences data integration vs data migration is crucial for choosing the right approach for your specific needs. This will also help ensure that […]View the full article

The importance of using data in sectors like Data Science, Machine Learning, etc. grows as the amount of data sources, and data types in an organization expand. Converting raw data into a clean and reliable form is a key step for extracting meaningful insights from it. ETL (Extract, Transform, and Load) is a Data Engineering […]View the full article

Making sure your technology stack works for you requires integration on a fundamental level. Everyone in your organization, from content writers who embed tweets into blog articles to data teams who reconcile data warehouses following a merger, can perform their duties more successfully with the help of coordinated data. Choosing the best tool for the […]View the full article

Today, businesses all around the world are driven by data. This has led to companies exploiting every available online application, service, and social platform to extract data to better understand the changing market trends. Now, this data requires numerous complex transformations to get ready for Data Analytics. Moreover, companies require technologies that can transfer and […]View the full article

We are thrilled to announce Unity Catalog Lakeguard , which allows you to run Apache Spark™ workloads in SQL, Python, and Scala with... View the full article

Data democratization may sound like just another technology buzzword, but with organizations collecting more and more data every day, the accuracy, trustworthiness, and... View the full article