The Top 5 Data Engineering Trends Heading into 2025
Data engineering is undergoing a transformative shift as we head into 2025. The demand for scalable, efficient, and AI-ready data architectures is at an all-time high. Advances in artificial intelligence, automation, and cloud computing are fueling these changes.
This e-book explores five significant trends that are set to reshape the future of data engineering and integration. From AI-powered pipelines to the resurgence of data lakes, these trends reflect the increasing complexity and importance of managing data in real time, at scale.
1. Data Pipelines for AI Applications
What It Is
AI applications are becoming increasingly prevalent, with organizations looking to use their data to power chatbots, AI copilots, and other automated systems. Data pipelines are essential for feeding these AI models with the necessary data, enabling real-time interactions and advanced analytics.
Why It Matters
AI can unlock new operational efficiencies and enhance customer experiences, but only if it is built on top of clean, accessible data. According to a report by McKinsey, AI could deliver an additional $13 trillion in economic output by 2030, representing a 1.2% annual GDP boost. However, for AI to be effective, businesses must have reliable data pipelines to feed unstructured data into AI workflows.
How It’s Done
Building data pipelines for AI applications requires ingesting data—often unstructured—from various sources, such as customer interactions, social media, and documents. Tools like Snowflake, Databricks, and specialized platforms such as Pinecone offer solutions for storing and querying this data in vector databases, which are particularly suited for AI models powered by RAG workflows.
Sample RAG workflow
Additionally, AWS provides abstracted solutions like Amazon Q, which simplifies building AI-driven applications using structured and unstructured data. With solutions like Amazon Q, data engineers don’t have to learn the mechanics of building a RAG workflow, instead, they just need to build a data pipeline that feeds the data into an Amazon S3 bucket and Amazon Q handles the rest.
Looking Forward
As AI use cases expand, the need for more sophisticated data pipelines will continue to grow. Data engineers must ensure that these pipelines can accommodate various data formats, especially as AI models become more complex.
With many options to handle the repeated components of RAG workflows including Snowflake,Databricks and Amazon with their own large language models (LLMs) and vector storage layers, The additional work for data engineers to serve AI data workflows will continue to be around building reliable data pipelines with platforms like Rivery that are uniquely positioned to assist organizations in ingesting data for AI-driven applications.
2. AI-Assisted Data Pipeline Development
What It Is
AI is not just transforming data pipelines for applications—it’s also streamlining the process of building these pipelines. AI-assisted tools can automate much of the tedious work traditionally done by data engineers, such as writing SQL queries or connecting to REST APIs.
Why It Matters
The introduction of AI tools for pipeline development has the potential to drastically reduce the time and expertise needed for pipeline creation. According to IDC, 40% of new data pipeline development efforts in 2025 will involve some form of AI assistance. This allows organizations to shift resources from routine tasks to more strategic work.
How It’s Done
Data platforms have introduced AI tools (Copilot and others) that generate transformation blocks, write SQL queries from natural language prompts, and automate documentation and testing for data workflows, ensuring data quality at scale. For example, Snowflake released a Copilot to help construct and understand SQL queries via prompts and Rivery’s Copilot leverages AI to build data integrations from REST APIs, enabling connections to data sources 30 times faster.
Snowflake’s Copilot to generate SQL queries using prompts
Looking Forward
As AI continues to integrate into data pipeline development, it will empower data analysts to perform more advanced transformation tasks, traditionally reserved for data engineers. This democratization of data engineering will free up engineers to focus on more complex problems, accelerating innovation in data science and analytics.
3. Platformization of Data Management
What It Is
The concept of “platformization” refers to the expansion of data management platforms to offer a one-stop shop for all data needs, from ingestion to transformation to orchestration. This reduces the complexity and cost of managing multiple tools across an organization’s data infrastructure.
Why It Matters
Many organizations have traditionally used a patchwork of 8-12 different vendors to build their data stacks, leading to increased costs and operational overhead. According to a survey by Dataversity, 64% of companies are seeking to consolidate their data management tools into fewer platforms.
How It’s Done
Large vendors like Snowflake, Databricks, and Microsoft have responded to this demand by expanding their product offerings. Snowflake’s dynamic tables, git Integration, and Microsoft Fabric bundling multiple Microsoft data services, are examples of this trend toward platformization. These platforms now offer more transformation capabilities, eliminating the need for multiple tools. Although these capabilities are still early in their development, they are signs of where the space is trending towards.
Snowflake’s Dynamic tables for continuous data transformation
Looking Forward
As the platformization continues, data users will shift their preferences to fewer tools that can accomplish multiple tasks (i.e. ingestion and orchestration in a single platform) as well as integrate with the large vendors.
4. The Resurgence of Data Lakes
What It Is
After a period of decline, data lakes are making a comeback thanks to technologies like Apache Iceberg and Delta Lake. These frameworks enable efficient querying of large datasets stored in cheap, scalable storage environments.
Why It Matters
Data lakes are ideal for storing vast amounts of structured and unstructured data, which is essential for AI and machine learning workloads. Research from IDC estimates that 80% of enterprise data will be unstructured by 2025, making data lakes critical for future data architectures. These formats (especially Iceberg which is getting traction among both Snowflake and Databricks), are increasing interoperability for data teams.
How It’s Done
Snowflake’s ability to query external Iceberg tables and Databricks’ acquisition of Tabular, a managed Iceberg vendor, are examples of how major platforms are embracing these data lake formats. This trend is expected to continue as more organizations migrate their data to scalable, cost-effective lake storage solutions.
Databricks’ vision to serve all with its ability to handle data in Iceberg, Delta Lake, and Hudi.
Looking Forward
As data volumes grow, especially with the expansion of AI, more organizations will shift from cloud data warehouses to data lakes for their storage needs. ETL vendors like Rivery will begin to support direct ingestion into Iceberg and other formats.
5. Direct Integrations and Zero ETL
What It Is
“Zero ETL” refers to the growing number of applications that allow users to access and analyze data without needing to move it, thanks to native integrations with cloud data warehouses (CDWs). This trend offers users a simpler, more cost-effective way to manage their data pipelines.
Why It Matters
Direct integrations reduce the need for complex ETL processes and minimize the risk of creating data silos. According to a 2024 study by Forrester, 60% of enterprises are adopting zero-ETL approaches to reduce their data processing costs.
How It’s Done
Leading applications like Salesforce, HubSpot, and Heap offer data-sharing capabilities that enable users to query their data directly in Snowflake without needing to transfer it. Google and AWS also provide zero-ETL/seamless integrations between their services, such as Google Analytics to BigQuery and Amazon Aurora to Redshift. This is helping in making it easier for users to access their data without the complexities of ETL. However, these solutions still create the potential for siloed data pipelines that are difficult to manage.
Sample integration between Amazon Aurora and Amazon Redshift
Looking Forward
Zero-ETL will continue to gain popularity due to its cost savings and ease of use, but it comes with challenges in managing multiple siloed integrations and maintaining historical snapshots of data. ELT providers like Rivery will be crucial in providing seamless, scalable integration solutions that support more complex data models and offer a central location for data teams to manage data pipelines.
Moving Forward For Data Engineers
The data engineering landscape in 2025 will be defined by AI, automation, and platform-driven solutions. As businesses face the growing challenge of managing massive data volumes, AI-powered tools, direct integrations, and scalable data lakes will become indispensable.
Although we’re still in the early stages of generative AI, its impact is clear: data-driven decision-making will soon be faster, more accessible, and seamlessly integrated across the entire data lifecycle. Data teams that embrace GenAI will lead this new era, while those that resist may struggle to keep pace.
The rise of AI promises to enhance efficiency in ELT processes, and adopting a platform-based approach will be key to excelling in both analytics and AI initiatives.
At Rivery, we are committed to this transformation. We’ve integrated generative AI into our workflows and product, reimagining data pipelines to be AI-powered. With our Copilot, connecting to any data source with a REST API is effortless, even if we don’t have a native connector.
Our goal is to make accessing all your data simple, regardless of your data engineering expertise. Stay tuned for more as we continue to expand AI integration, helping users unlock even more potential from their data.
