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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. To start the job, choose Run. format(dbname)).config("spark.sql.catalog.glue_catalog.catalog-impl",
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. Can it also help write SQL queries?
In this post, we share part of the journey that Jumia took with AWS Professional Services to modernize its data platform that ran under a Hadoop distribution to AWS serverless based solutions. These phases are: data orchestration, data migration, data ingestion, data processing, and data maintenance.
Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. Eventually, transactional datalakes emerged to add transactional consistency and performance of a data warehouse to the datalake.
To achieve this, they aimed to break down data silos and centralize data from various business units and countries into the BMW Cloud Data Hub (CDH). However, the initial version of CDH supported only coarse-grained access control to entire data assets, and hence it was not possible to scope access to data asset subsets.
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. However, commits can still fail if the latest metadata is updated after the base metadata version is established.
Iceberg offers distinct advantages through its metadata layer over Parquet, such as improved data management, performance optimization, and integration with various query engines. As mentioned earlier, 80% of quantitative research work is attributed to data management tasks.
Amazon Redshift , launched in 2013, has undergone significant evolution since its inception, allowing customers to expand the horizons of data warehousing and SQL analytics. Industry-leading price-performance Amazon Redshift offers up to three times better price-performance than alternative cloud data warehouses.
This interoperability is crucial for enabling seamless data access, reducing data silos, and fostering a more flexible and efficient data ecosystem. Delta Lake UniForm is an open table format extension designed to provide a universal data representation that can be efficiently read by different processing engines.
Their terminal operations rely heavily on seamless data flows and the management of vast volumes of data. Recently, EUROGATE has developed a digital twin for its container terminal Hamburg (CTH), generating millions of data points every second from Internet of Things (IoT)devices attached to its container handling equipment (CHE).
Amazon SageMaker has announced an integration with Amazon QuickSight , bringing together data in SageMaker seamlessly with QuickSight capabilities like interactive dashboards, pixel perfect reports and generative business intelligence (BI)—all in a governed and automated manner. Select the Blueprints tab.
The open data lakehouse is quickly becoming the standard architecture for unified multifunction analytics on large volumes of data. It combines the flexibility and scalability of datalake storage with the data analytics, data governance, and data management functionality of the data warehouse.
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.
Streamlining workflows and boosting productivity The next set of re:Invent announcements focused on streamlining workflows for enterprises and helping businesses boost the productivity of developers and data professionals. On the storage front, AWS unveiled S3 Table Buckets and the S3 Metadata features.
They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. This innovation drives an important change: you’ll no longer have to copy or move data between datalake and data warehouses.
As businesses generate more data from a variety of sources, they need systems to effectively manage that data and use it for business outcomes—such as providing better customer experiences or reducing costs. Iceberg brings the reliability and simplicity of SQL tables to Amazon Simple Storage Service (Amazon S3) datalakes.
Datalakes were originally designed to store large volumes of raw, unstructured, or semi-structured data at a low cost, primarily serving big data and analytics use cases. By using features like Icebergs compaction, OTFs streamline maintenance, making it straightforward to manage object and metadata versioning at scale.
With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.
It’s a set of HTTP endpoints to perform operations such as invoking Directed Acyclic Graphs (DAGs), checking task statuses, retrieving metadata about workflows, managing connections and variables, and even initiating dataset-related events, without directly accessing the Airflow web interface or command line tools.
This shift requires shorter time to value and tighter collaboration among data analysts, data scientists, machine learning (ML) engineers, and application developers. However, the reality of scattered data across various systems—from datalakes to data warehouses and applications—makes it difficult to access and use data efficiently.
A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Mainframes hold an enormous amount of critical and sensitive business data including transactional information, healthcare records, customer data, and inventory metrics.
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.
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. These controls are designed to grant access with the right level of privileges and context.
Data quality is no longer a back-office concern. We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. One thing is clear for leaders aiming to drive trusted AI, resilient operations and informed decisions at scale: transformation starts with data you can trust.
Amazon DataZone now launched authentication supports through the Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed datalake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more.
Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. It also helps you securely access your data in operational databases, datalakes, or third-party datasets with minimal movement or copying of data.
Increasingly, organizations are adopting Apache Iceberg, an open source table format that simplifies data processing on large datasets stored in datalakes. It seamlessly integrates with popular open source big data processing frameworks Apache Spark , Apache Hive , Apache Flink , Presto , and Trino.
This approach simplifies your data journey and helps you meet your security requirements. The SageMaker Lakehouse data connection testing capability boosts your confidence in established connections. Choose Add data. Prerequisites Before you begin, make sure you have the followings: An AWS account. Choose Save changes.
In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products.
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).
To address these challenges and enhance developer productivity, we’re excited to introduce the AWS Data Processing MCP Server , an open-source tool that uses the Model Context Protocol (MCP) to simplify analytics environment setup on AWS. The MCP server interprets this request and automatically constructs the appropriate analytical workflow.
You can secure and centrally manage your data in the lakehouse by defining fine-grained permissions with Lake Formation that are consistently applied across all analytics and machine learning(ML) tools and engines. These tags are assigned to IAM users or roles and can be used to define or restrict access to specific resources or data.
As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. Common use cases for historical record management in CDC scenarios span various domains.
During development, data engineers often spend hours sifting through log files, analyzing execution plans, and making configuration changes to resolve issues. Troubleshooting these production issues requires extensive analysis of logs and metrics, often leading to extended downtimes and delayed insights from critical data pipelines.
The next generation of Amazon SageMaker is the center for all your data, analytics, and AI. Amazon SageMaker Unified Studio is a single data and AI development environment where you can find and access all of the data in your organization and act on it using the best tools across any use case. First, create a Notebook.
Like many manufacturing companies, Furuno Electric faced significant changes in revenue structure and technical architecture as they transitioned from traditional business to data-driven business. To succeed in this transformation, it was essential to build a foundation that promotes data utilization across the entire organization.
However, many companies today still struggle to effectively harness and use their data due to challenges such as data silos, lack of discoverability, poor data quality, and a lack of data literacy and analytical capabilities to quickly access and use data across the organization.
Create an Amazon Bedrock knowledge base referencing structured data in Amazon Redshift Amazon Bedrock Knowledge Bases uses Amazon Redshift as the query engine to query your data. It reads metadata from your structured data store to generate SQL queries. For this demo, we use Amazon Bedrock to access the Amazon Nova FMs.
Quick setup enables two default blueprints and creates the default environment profiles for the datalake and data warehouse default blueprints. The script creates a table with sample marketing and sales data. You will then publish the data assets from these data sources. AS wholesale_cost, 45.0
Similarly, the orphan file deletion process scans the table metadata and the actual data files, identifies the unreferenced files, and deletes them to reclaim storage space. These storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance.
Given the importance of data in the world today, organizations face the dual challenges of managing large-scale, continuously incoming data while vetting its quality and reliability. One of its key features is the ability to manage data using branches. One of its key features is the ability to manage data using branches.
In today’s rapidly evolving financial landscape, data is the bedrock of innovation, enhancing customer and employee experiences and securing a competitive edge. Like many large financial institutions, ANZ Institutional Division operated with siloed data practices and centralized data management teams.
Data engineers use data warehouses, datalakes, and analytics tools to load, transform, clean, and aggregate data. Data scientists use notebook environments (such as JupyterLab) to create predictive models for different target segments.
Cloudera’s open data lakehouse, powered by Apache Iceberg, solves the real-world big data challenges mentioned above by providing a unified, curated, shareable, and interoperable datalake that is accessible by a wide array of Iceberg-compatible compute engines and tools. spark.sql(SELECT * FROM airlines_data.carriers).show()
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