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Summary Metadata is the lifeblood of your data platform, providing information about what is happening in your systems. In order to level up their value a new trend of active metadata is being implemented, allowing use cases like keeping BI reports up to date, auto-scaling your warehouses, and automated data governance.
Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , datalake and data lakehouse , and distributed patterns such as data mesh.
In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI.
Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms. In this blog, we will discuss: What is the Open Table format (OTF)? First, we create an Iceberg table in Snowflake and then insert some data.
[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. I found the blog to be a comprehensive roadmap for data engineering in 2025.
Apache Iceberg’s ecosystem of diverse adopters, contributors and commercial support continues to grow, establishing itself as the industry standard table format for an open data lakehouse architecture. Snowflake’s support for Iceberg Tables is now in public preview, helping customers build and integrate Snowflake into their lake architecture.
The Grab blog delights me since I have tried to do this many times. A cross-encoder teacher model, fine-tuned on human-labeled data and enriched Pin metadata, was distilled into a lightweight student model using semi-supervised learning over billions of impressions. Kudos to the Grab team for building a docs-as-code system.
Learn More → Notion: Building and scaling Notion’s datalake Notion writes about scaling the datalake by bringing critical data ingestion operations in-house. Hudi seems to be a de facto choice for CDC datalake features. Notion migrated the insert heavy workload from Snowflake to Hudi.
While data warehouses are still in use, they are limited in use-cases as they only support structured data. Datalakes add support for semi-structured and unstructured data, and data lakehouses add further flexibility with better governance in a true hybrid solution built from the ground-up.
With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a DataLake? Consistency of data throughout the datalake.
In an effort to better understand where data governance is heading, we spoke with top executives from IT, healthcare, and finance to hear their thoughts on the biggest trends, key challenges, and what insights they would recommend. This blog is a collection of those insights, but for the full trendbook, we recommend downloading the PDF.
Apache Ozone is one of the major innovations introduced in CDP, which provides the next generation storage architecture for Big Data applications, where data blocks are organized in storage containers for larger scale and to handle small objects. Collects and aggregates metadata from components and present cluster state.
Catalog Integration: Our newly developed Catalog Integration feature allows you to seamlessly plug Snowflake into other Iceberg catalogs tracking table metadata. In this blog post, we’ll dive into the details of these features and the benefits for customers. In addition to Iceberg External Tables, we introduced Native Iceberg Tables.
Over the past few years, datalakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.
In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. Data Ingestion. The raw data is in a series of CSV files. We will firstly convert this to parquet format as most datalakes exist as object stores full of parquet files.
In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. Data and Metadata: Data inputs and data outputs produced based on the application logic.
Cloudera customers run some of the biggest datalakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and datalakes.
This blog post outlines detailed step by step instructions to perform Hive Replication from an on-prem CDH cluster to a CDP Public Cloud DataLake. CDP DataLake cluster versions – CM 7.4.0, CDP DataLake cluster versions – CM 7.4.0, Pre-Check: DataLake Cluster.
Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. RudderStack helps you build a customer data platform on your warehouse or datalake.
With Cloudera’s vision of hybrid data , enterprises adopting an open data lakehouse can easily get application interoperability and portability to and from on premises environments and any public cloud without worrying about data scaling. Why integrate Apache Iceberg with Cloudera Data Platform?
Change Data Capture (CDC) has emerged as an ideal solution for near real-time movement of data from relational databases (like SQL Server or Oracle) to data warehouses, datalakes or other databases. Data can be extracted using database queries (batch-based) or Change Data Capture (near-real-time).
With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Proprietary file formats mean no one else is invited in!
When you register an Environment in CDP, a DataLake is automatically deployed for that environment. DataLake security and governance is managed by a shared set of services running within a DataLake cluster. Apache Atlas — metadata management and governance: lineage, analytics, attributes.
You'll be seen as the most technical person of a data team and you'll need to help regarding "low-level" stuff you team. You'll be also asked to put in place a data infrastructure. It means a data warehouse, a datalake or other concepts starting with data. Is it really modern?
Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.
analyst Sumit Pal, in “Exploring Lakehouse Architecture and Use Cases,” published January 11, 2022: “Data lakehouses integrate and unify the capabilities of data warehouses and datalakes, aiming to support AI, BI, ML, and data engineering on a single platform.” Iceberg handles massive data born in the cloud.
ELT – keep all source tables and use DBT for converting relevant tables into star/snowflake/data vault/wide tables) What are your thoughts on the viability of a datalake as the destination system? e.g. APIs and third party data sources How can we integrage CDC into metadata/lineage tooling?
The Solution: CDP Private Cloud brings a next-generation hybrid architecture with cloud-native benefits to HBL’s data platform. HBL started their data journey in 2019 when datalake initiative was started to consolidate complex data sources and enable the bank to use single version of truth for decision making.
Access audits are mastered centrally in Apache Ranger which provides comprehensive non-repudiable audit log for every access event to every resource with rich access event metadata such as: IP. Both fine-grained access control of database objects and access to metadata is provided. Sensitive data identification.
Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and datalakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Forrester ).
The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.
CSP was recently recognized as a leader in the 2022 GigaOm Radar for Streaming Data Platforms report. The DevOps/app dev team wants to know how data flows between such entities and understand the key performance metrics (KPMs) of these entities. She is a smart data analyst and former DBA working at a planet-scale manufacturing company.
Customers who have chosen Google Cloud as their cloud platform can now use CDP Public Cloud to create secure governed datalakes in their own cloud accounts and deliver security, compliance and metadata management across multiple compute clusters. You can get started with CDP Public Cloud by requesting a trial account here.
First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt. Second-generation – gigantic, complex datalake maintained by a specialized team drowning in technical debt. The post What is a Data Mesh?
Overview This blog post describes support for materialized views for the Iceberg table format. It brings the reliability and simplicity of SQL tables to big data while enabling engines like Hive, Impala, Spark, Trino, Flink, and Presto to work with the same tables at the same time. These tables are created as Iceberg tables.
While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ datalake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.
Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.
With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .
Cloudera has long had the capabilities of a data lakehouse, if not the label. Cloudera enables an open data lakehouse architecture that combines all the flexibility of the datalake with the performance of the data warehouse, so enterprises can use all data — both structured and unstructured.
Cloudera Data Platform (CDP) scored among the top 10 vendors on all four Analytical Use Cases — Data Warehouse, Logical Data Warehouse, DataLake and Operational Intelligence in the Critical Capabilities for Cloud Database Management Systems for Analytics Use Cases.
The table information (such as schema, partition) is stored as part of the metadata (manifest) file separately, making it easier for applications to quickly integrate with the tables and the storage formats of their choice. The post 5 Reasons to Use Apache Iceberg on Cloudera Data Platform (CDP) appeared first on Cloudera Blog.
Some examples of recent optimizations in Impala include: New multithreading model (see dedicated blog post ). Remote read optimizations: IMPALA-8341: Data cache for remote reads. Impala use of KRPC (see dedicated blog post ). Parquet page indexes (see dedicated blog post ). IMPALA-8690: Add LIRS cache eviction algorithm.
I took the free version of ChatGPT on a test drive (in March 2023) and asked some simple questions on data lakehouse and its components. Hopefully this blog will give ChatGPT an opportunity to learn and correct itself while counting towards my 2023 contribution to social good. I thought this was a fairly comprehensive list.
The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., ….
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