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This is part two of a three-part series where we show how to build a datalake on AWS using a modern data architecture. This post shows how to load data from a legacy database (SQL Server) into a transactional datalake ( Apache Iceberg ) using AWS Glue.
Enterprise data is brought into datalakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on. Table metadata is fetched from AWS Glue. The generated Athena SQL query is run.
In modern data architectures, Apache Iceberg has emerged as a popular table format for datalakes, offering key features including ACID transactions and concurrent write support. We will also cover the pattern with automatic compaction through AWS Glue Data Catalog table optimization. Generate new metadata files.
Iceberg offers distinct advantages through its metadata layer over Parquet, such as improved data management, performance optimization, and integration with various query engines. Unlike direct Amazon S3 access, Iceberg supports these operations on petabyte-scale datalakes without requiring complex custom code.
Today, Amazon Redshift is used by customers across all industries for a variety of use cases, including data warehouse migration and modernization, near real-time analytics, self-service analytics, datalake analytics, machine learning (ML), and data monetization. We have launched new RA3.large large instances.
In the era of big data, datalakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources.
Cloudinary is a cloud-based media management platform that provides a comprehensive set of tools and services for managing, optimizing, and delivering images, videos, and other media assets on websites and mobile applications. This concept makes Iceberg extremely versatile.
A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a datalake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.
Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in datalakes and features like schema and partition evolution, time travel, and rollback.
Amazon Redshift enables you to efficiently query and retrieve structured and semi-structured data from open format files in Amazon S3 datalake without having to load the data into Amazon Redshift tables. Amazon Redshift extends SQL capabilities to your datalake, enabling you to run analytical queries.
Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. By providing a standardized framework for data representation, open table formats break down data silos, enhance data quality, and accelerate analytics at scale.
For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog. This process is shown in the following figure.
Amazon Q generative SQL for Amazon Redshift uses generative AI to analyze user intent, query patterns, and schema metadata to identify common SQL query patterns directly within Amazon Redshift, accelerating the query authoring process for users and reducing the time required to derive actionable data insights.
Amazon Redshift enables you to directly access data stored in Amazon Simple Storage Service (Amazon S3) using SQL queries and join data across your data warehouse and datalake. With Amazon Redshift, you can query the data in your S3 datalake using a central AWS Glue metastore from your Redshift data warehouse.
In our previous post Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 datalakes , we discussed how you can implement solutions to improve operational efficiencies of your Amazon Simple Storage Service (Amazon S3) datalake that is using the Apache Iceberg open table format and running on the Amazon EMR big data platform.
Many organizations operate datalakes spanning multiple cloud data stores. In these cases, you may want an integrated query layer to seamlessly run analytical queries across these diverse cloud stores and streamline your data analytics processes. The AWS Glue Data Catalog holds the metadata for Amazon S3 and GCS data.
The AWS Glue Data Catalog now enhances managed table optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Iceberg creates a new version called a snapshot for every change to the data in the table. As more table changes are made, more data files are created.
We often see requests from customers who have started their data journey by building datalakes on Microsoft Azure, to extend access to the data to AWS services. In such scenarios, data engineers face challenges in connecting and extracting data from storage containers on Microsoft Azure.
Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Datalakes have served as a central repository to store structured and unstructured data at any scale and in various formats.
Unlocking the true value of data often gets impeded by siloed information. Traditional data management—wherein each business unit ingests raw data in separate datalakes or warehouses—hinders visibility and cross-functional analysis. Amazon DataZone natively supports data sharing for Amazon Redshift data assets.
A modern data architecture is an evolutionary architecture pattern designed to integrate a datalake, data warehouse, and purpose-built stores with a unified governance model. Moreover, the framework should consume compute resources as optimally as possible per the size of the operational tables.
With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.
When you build your transactional datalake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 datalake to optimize the production environment. This property is set to true by default. availability.
With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional datalake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.
For many organizations, this centralized data store follows a datalake architecture. Although datalakes provide a centralized repository, making sense of this data and extracting valuable insights can be challenging.
Analytics remained one of the key focus areas this year, with significant updates and innovations aimed at helping businesses harness their data more efficiently and accelerate insights. From enhancing datalakes to empowering AI-driven analytics, AWS unveiled new tools and services that are set to shape the future of data and analytics.
You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) datalake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python. Let’s discuss some of the cost-based optimization techniques that contributed to improved query performance.
We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.
Within the ANZ enterprise data mesh strategy, aligning data mesh nodes with the ANZ Group’s divisional structure provides optimal alignment between data mesh principles and organizational structure, as shown in the following diagram. Consumer feedback and demand drives creation and maintenance of the data product.
To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the datalake. What’s in a DataLake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.
AWS Lake Formation and the AWS Glue Data Catalog form an integral part of a data governance solution for datalakes built on Amazon Simple Storage Service (Amazon S3) with multiple AWS analytics services integrating with them. In 2022 , we talked about the enhancements we had done to these services. Bien intégré!
In today’s world, customers manage vast amounts of data in their Amazon Simple Storage Service (Amazon S3) datalakes, which requires convoluted data pipelines to continuously understand the changes in the data layout and make them available to consuming systems.
Although Jira Cloud provides reporting capability, loading this data into a datalake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. For InitialRunFlag , choose Setup.
For datalake customers who need to discover petabytes of data, AWS Glue crawlers are a popular way to discover and catalog data in the background. This allows users to search and find relevant data from multiple data sources. Choose the table to view the schema and other metadata.
However, they do contain effective data management, organization, and integrity capabilities. As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Warehouse, datalake convergence. Meet the data lakehouse.
DataLakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic datalake architecture Datalakes are, at a high level, single repositories of data at scale.
However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture. In this context, Amazon DataZone is the optimal choice for managing the enterprise data platform.
Apache Hudi is an open table format that brings database and data warehouse capabilities to datalakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. For CoW tables, queries see the latest data committed.
These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (APIs), file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.
AWS-powered datalakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. It will never remove files that are still required by a non-expired snapshot.
At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and datalakes can become equally challenging.
For many enterprises, a hybrid cloud datalake is no longer a trend, but becoming reality. Not only can resources be quickly provisioned and optimized for different workloads and processing needs, but it can be done cost effectively. The Alation Data Catalog will automatically crawl and catalog metadata in your S3 bucket(s).
Modernizing analytics for scale, performance, and reliability “Our migration from legacy on-premises platform to Amazon Redshift allows us to ingest data 88% faster, query data 3x faster, and load daily data to the cloud 6x faster.
BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse. Amazon Redshift is a fully managed data warehouse service offered by Amazon Web Services (AWS).
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