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Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. Datalakes are notoriously complex.
In this episode David Yaffe and Johnny Graettinger share the story behind the business and technology and how you can start using it today to build a real-time datalake without all of the headache. What is the impact of continuous data flows on dags/orchestration of transforms? RudderStack also supports real-time use cases.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or datalake. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines.
It addresses many of Kafka's challenges in analytical infrastructure. The combination of Kafka and Flink is not a perfect fit for real-time analytics; the integration of Kafka and Lakehouse is very shallow. How do you compare Fluss with Apache Kafka? Fluss and Kafka differ fundamentally in design principles.
Summary Datalake architectures have largely been biased toward batch processing workflows due to the volume of data that they are designed for. With more real-time requirements and the increasing use of streaming data there has been a struggle to merge fast, incremental updates with large, historical analysis.
Ingest data more efficiently and manage costs For data managed by Snowflake, we are introducing features that help you access data easily and cost-effectively. This reduces the overall complexity of getting streaming data ready to use: Simply create external access integration with your existing Kafka solution.
Summary The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of datalakes as a solution for managing storage and access.
Summary Datalakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their datalake platform.
Summary A datalake can be a highly valuable resource, as long as it is well built and well managed. In this episode Yoni Iny, CTO of Upsolver, discusses the various components that are necessary for a successful datalake project, how the Upsolver platform is architected, and how modern datalakes can benefit your organization.
Summary Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a datalake as its central architectural tenet adds additional layers of difficulty. When is a datalake architecture the wrong choice?
The ksqlDB project was created to address this state of affairs by building a unified layer on top of the Kafka ecosystem for stream processing. Developers can work with the SQL constructs that they are familiar with while automatically getting the durability and reliability that Kafka offers. How is ksqlDB architected?
In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex.
In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another datalake. However, Apache Kafka is more than just messaging. Some Kafka and Rockset users have also built real-time e-commerce applications , for example, using Rockset’s Java, Node.js
We also discuss the various systems using Kafka’s protocol. Confluent has never shied away from saying Kafka is “easy,” and I disagree. During the Kafka Summit London Keynote, the speakers said “easy” 17 times; in the Kafka Summit Bangalore Keynote, they said it 18 times. Want to use the Kafka protocol with Pulsar?
It incorporates elements from several Microsoft products working together, like Power BI, Azure Synapse Analytics, Data Factory, and OneLake, into a single SaaS experience. No matter the workload, Fabric stores all data on OneLake, a single, unified datalake built on the Delta Lake model.
[link] Alireza Sadeghi: Open Source Data Engineering Landscape 2025 This article comprehensively overviews the 2025 open-source data engineering landscape, highlighting key trends, active projects, and emerging technologies. The proposal discusses how Kafka will implement queue functionality similar to SQS and RabbitMQ.
Trains are an excellent source of streaming data—their movements around the network are an unbounded series of events. Using this data, Apache Kafka ® and Confluent Platform can provide the foundations for both event-driven applications as well as an analytical platform. As with any real system, the data has “character.”
The approach bridges the data and software engineering gap, offering a practical blueprint for scaling trustworthy data systems. link] ManoMano: Handle errors in Spring Kafka consumers like a bliss - retry and DLT reporting for duty The tiered-topic approach to handling backoff and DLQ made me think deeply about the pattern.
Tableflow represents Kafka topics as Apache Iceberg (GA) and Delta Lake (EA) tables in a few clicks to feed any data warehouse, datalake, or analytics engine of your choice
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. Powered by Trino, the query engine Apache Iceberg was designed for, Starburst is an open platform with support for all table formats including Apache Iceberg, Hive, and Delta Lake.
Datalakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
The people behind Apache Kafka asked themselves the same question , so they invented the Kappa Architecture, where instead of having both batching and streaming layers, everything is real-time with the whole stream of data stored in a central log like Kafka.
Key features include workplan auctioning for resource allocation, in-progress remediation for handling data validation failures, and integration with external Kafka topics, achieving a throughput of 1.2 million entities per second in production.
Datalakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a datalake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.
A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : DataLake, Data Warehouse, and Data Mart. Data Layers can be often combined together, sometimes in a single platform.
“DataLake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms datalake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Datalake? What is a Datalake?
In 2010, a transformative concept took root in the realm of data storage and analytics — a datalake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a datalake?
They no longer need to ask a small subset of the organization to provide them with information, rather, they have tooling, systems, and capabilities to get the data they need. Data Democratization has been a topic of conversation for the last few years – but mostly centered around data warehousing and datalakes.
In 2015, Cloudera became one of the first vendors to provide enterprise support for Apache Kafka, which marked the genesis of the Cloudera Stream Processing (CSP) offering. Today, CSP is powered by Apache Flink and Kafka and provides a complete, enterprise-grade stream management and stateful processing solution. Who is affected?
The company quickly realized maintaining 10 years’ worth of production data while enabling real-time data ingestion led to an unscalable situation that would have necessitated a datalake. Snowflake's separate clusters for ETL, reporting and data science eliminated resource contention.
Summary Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a datalake with a versioned file system. Interview with Daniel Whitenack Introduction How did you get started in the data engineering space?
We offer both historical and low-latency data streams of on-chain data across multiple blockchains. How we use Apache Kafka and the Confluent Platform. Apache Kafka ® is the central data hub of our company. Cluster of Ethereum nodes, Ethereum-to-Kafka bridge. Block confirmer based on Kafka Streams.
Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. RudderStack helps you build a customer data platform on your warehouse or datalake. RudderStack helps you build a customer data platform on your warehouse or datalake.
Data Engineering Tools Data engineers need to be comfortable using essential tools for data pipeline management and workflow orchestration, including Apache Kafka, Apache Spark, Airflow, Dagster, dbt, and many more. Data Storage Solutions As we all know, data can be stored in a variety of ways.
As capable as it is, there are still instances where MongoDB alone can't satisfy all of the requirements for an application, so getting a copy of the data into another platform via a change data capture (CDC) solution is required. Debezium It is also possible to capture MongoDB change data capture events using Debezium.
Unbound by the limitations of a legacy on-premises solution, its multi-cluster shared data architecture separates compute from storage, allowing data teams to easily scale up and down based on their needs. With Snowflake’s Kafka connector, the technology team can ingest tokenized data as JSON into tables as VARIANT.
In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Start trusting your data with Monte Carlo today!
In this category I recommend also to have a look at data ingestion (Airbyte, Fivetran, etc.), Main technologies around stream are bus messages like Kafka and processing framework like Flink or Spark on top of the bus. Understand Change Data Capture — CDC. You'll be also asked to put in place a data infrastructure.
Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or datalake. Email hosts@dataengineeringpodcast.com ) with your story.
Data ingestion pipeline with Operation Management — At Netflix they annotate video which can lead to thousand of annotation but they need to manage the annotation lifecycle each time the annotation algorithm runs. Some company also call it a lakehouse or a datalake, but the word shift is enough interesting to notice.
Summary Analytical workloads require a well engineered and well maintained data integration process to ensure that your information is reliable and up to date. Building a real-time pipeline for your datalakes and data warehouses is a non-trivial effort, requiring a substantial investment of time and energy.
Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide datalakes versus smaller, typically BU-Specific, “data ponds”.
From origin through all points of consumption both on-prem and in the cloud, all data flows need to be controlled in a simple, secure, universal, scalable, and cost-effective way. controlling distribution while also allowing the freedom and flexibility to deliver the data to different services is more critical than ever. .
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