Gary Baverstock’s Post

One of the critical ideas behind data fabric is the capability to access any data asset in the organization through a central, easy-to-use access point. This can be achieved using a data virtualization layer that abstracts the complexity and offers a central access point. #Datafabric vendors implement two main architectures to provide this capability: specialized #datavirtualization layers; and data engines with #data virtualization extensions. Download this @Denodo whitepaper, where we will explore both architectures in detail, and we will focus on the implications that these implementation decisions have in terms of the performance of query execution https://buff.ly/4criIto

One of the critical ideas behind data fabric is the capability to access any data asset in the organization through a central, easy-to-use access point. This can be achieved using a data virtualization layer that abstracts the complexity and offers a central access point. #Datafabric vendors implement two main architectures to provide this capability: specialized #datavirtualization layers; and data engines with #data virtualization extensions. Download this Denodo whitepaper, where we will explore both architectures in detail, and we will focus on the implications that these implementation decisions have in terms of the performance of query execution https://buff.ly/4criIto

One of the critical ideas behind data fabric is the capability to access any data asset in the organization through a central, easy-to-use access point. This can be achieved using a data virtualization layer that abstracts the complexity and offers a central access point. #Datafabric vendors implement two main architectures to provide this capability: specialized #datavirtualization layers; and data engines with #data virtualization extensions. Download this Denodo whitepaper, where we will explore both architectures in detail, and we will focus on the implications that these implementation decisions have in terms of the performance of query execution https://buff.ly/4criIto

One of the critical ideas behind data fabric is the capability to access any data asset in the organization through a central, easy-to-use access point. This can be achieved using a data virtualization layer that abstracts the complexity and offers a central access point. #Datafabric vendors implement two main architectures to provide this capability: specialized #datavirtualization layers; and data engines with #data virtualization extensions. Download this Denodo whitepaper, where we will explore both architectures in detail, and we will focus on the implications that these implementation decisions have in terms of the performance of query execution https://buff.ly/4criIto

One of the critical ideas behind data fabric is the capability to access any data asset in the organization through a central, easy-to-use access point. This can be achieved using a data virtualization layer that abstracts the complexity and offers a central access point. #Datafabric vendors implement two main architectures to provide this capability: specialized #datavirtualization layers; and data engines with #data virtualization extensions. Download this Denodo whitepaper, where we will explore both architectures in detail, and we will focus on the implications that these implementation decisions have in terms of the performance of query execution https://buff.ly/4criIto #データ仮想化 ＃データ統合 ＃データ連携 ＃データマネジメント

Performance in a Data Fabric: Comparing Data Virtualization Architectures - Understand the differences between #datavirtualization layers and data engines with #data virtualization extensions. https://lnkd.in/d7kbGWaM

In this whitepaper we compare the performance of Denodo with alternative architectures that were originally born in the Data Lake, using the standard TPC-H benchmark. Really interesting read for all interested in understanding performance in #datafabric and #datavirtualization https://lnkd.in/eJqbJF3a

Now we have public evidences of what we already know. It’s not just about the concept of #Datafabric or the #semanticlayer with distributed data. To be able to execute properly with the needed performance and agility to make it sustainable and viable at enterprise level. More than 20 years of experience and focus on that techniques brings that benefits.

Why use an external buffer when performing data replication? SO many benefits. Here are a few to call out: 1️⃣ Load reduction on source database. Ability to capture changes and store them temporarily outside the database, so the database doesn’t have to handle direct read requests from the replication process. 2️⃣ Improved fault tolerance. Replication process is more robust against failures, as the buffer can be re-processed without needing to re-query the source database. 3️⃣ Efficient batch processing. For certain workloads that don’t require real-time replication, using an external buffer to collect data to be sent in batches can significantly reduce networking and compute load. 4️⃣ Increased scalability. External buffers provide a flexible layer that can scale up and down based on data throughput. 5️⃣ Higher data consistency. It’s easier to ensure correct ordering and consistency, even if there are networking disruptions or processing delays. 6️⃣ Conflict resolution. In multi-master replication scenarios, the external buffer can resolve conflicts as it’s a neutral zone where data from different sources can be compared and merged according to predefined rules. We wrote about our architectural decisions when building Artie (YC S23) and having an external buffer was one of them. You can read more in the link below. #dataengineering #datareplication #data

🔄 Understanding Data Replication in Distributed Systems Data replication is crucial for building robust, scalable systems. Here's a quick overview: Why replicate? • Reduce latency • Ensure high availability • Scale read operations Key Replication Strategies: 1️⃣ Single-Leader • One node handles all writes • Simple, but potential bottleneck 2️⃣ Multi-Leader • Multiple write-accepting nodes • Great for multi-datacenter setups • Requires conflict resolution 3️⃣ Leaderless • Any node accepts reads/writes • Uses quorum for consistency Synchronous vs. Asynchronous: • Sync: Stronger consistency, potential performance hit • Async: Better performance, risk of data loss Handling Node Failures: • Implement failover mechanisms • Use read repair and anti-entropy processes • Employ quorum techniques Version Management: • Version vectors track concurrent updates Remember: Choose your replication strategy based on your specific needs, balancing consistency, availability, and partition tolerance. For Deep dive into data Replication See below 👇 https://lnkd.in/g-yyPQ8A #DataEngineering #DistributedSystems #DatabaseDesign