#hadoop-migration

#hadoop-migration

The resulting system now ingests over 100 million samples per second in production, showcasing the scalability and efficiency of the new metrics stack.

Apache Kafka has cemented itself as the de facto platform for event streaming, often referred to as the 'universal data substrate' due to its extensive ecosystem that enables connectivity and processing capabilities.

The model's other capabilities, including support for multimodal inputs, multiple reasoning modes, and parallel sub-agents for complex queries, could help enterprises build faster, task-focused AI for customer support, automation, and internal copilots without relying on heavier models.

Airflow 3 represents a clear architectural direction for the project: API-driven execution, better isolation, data-aware scheduling and a platform designed for modern scale. While Airflow 2.x is still widely used, it is clearly moving toward long-term maintenance (end-of-life April 2026) with most innovation and architectural investment happening in the 3.x line.

S3 Files is built on Amazon EFS and automatically translates file system operations into S3 requests, allowing applications to work with S3 data without code changes.

Automating Kubernetes workloads with Operators can provide the same level of functionality as DBaaS, while still avoiding lock-in to a specific provider.

Uber's engineering team has transformed its data replication platform to move petabytes of data daily across hybrid cloud and on-premise data lakes, addressing scaling challenges caused by rapidly growing workloads. Built on Hadoop's open-source Distcp framework, the platform now handles over one petabyte of daily replication and hundreds of thousands of jobs with improved speed, reliability, and observability.

Snowflake offers a fully managed data platform, but Sumo Logic users often lack insight into performance, login activity, and operational health. The Sumo Logic Snowflake Logs App analyzes login and access activity to identify anomalies or suspicious behavior. It also optimizes data pipelines with insights into long-running or failing queries. Teams can centralize log data to facilitate correlation across applications, cloud services, and data platforms.

Uber has built HiveSync, a sharded batch replication system that keeps Hive and HDFS data synchronized across multiple regions, handling millions of Hive events daily. HiveSync ensures cross-region data consistency, enables Uber's disaster recovery strategy, and eliminates inefficiency caused by the secondary region sitting idle, which previously incurred hardware costs equal to the primary, while still maintaining high availability. Built initially on the open-source Airbnb ReAir project, HiveSync has been extended with sharding, DAG-based orchestration, and a separation of control and data planes.

Databricks today announced the general availability of Lakebase on AWS, a new database architecture that separates compute and storage. The managed serverless Postgres service is designed to help organizations build faster without worrying about infrastructure management. When databases link compute and storage, every query must use the same CPU and memory resources. This can cause a single heavy query to affect all other operations. By separating compute and storage, resources automatically scale with the actual load.

A future-proof IT infrastructure is often positioned as a universal solution that can withstand any change. However, such a solution does not exist. Nevertheless, future-proofing is an important concept for IT leaders navigating continuous technological developments and security risks, all while ensuring that daily business operations continue. The challenge is finding a balance between reactive problem solving and proactive planning, because overlooking a change can cost your organization. So, how do you successfully prepare for the future without that one-size-fits-all solution?

Developers have spent the past decade trying to forget databases exist. Not literally, of course. We still store petabytes. But for the average developer, the database became an implementation detail; an essential but staid utility layer we worked hard not to think about. We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible.

The main advantage of going the Multi-Cloud way is that organizations can "put their eggs in different baskets" and be more versatile in their approach to how they do things. For example, they can mix it up and opt for a cloud-based Platform-as-a-Service (PaaS) solution when it comes to the database, while going the Software-as-a-Service (SaaS) route for their application endeavors.

Snowflake adds observability capabilities via Trail The company also added new observability features in the form of Snowflake Trail, which provides visibility into data quality, pipelines, and applications, enabling developers to monitor, troubleshoot, and optimize their workflows. It is built with OpenTelemetry standards so developers can integrate with popular observability and alert platforms including Datadog, Grafana, Metaplane, PagerDuty, and Slack, among others.