AWS Big Data

MARCH 29, 2024

Analyzing historical patterns allows you to optimize performance, identify issues proactively, and improve planning. Data Firehose uses an AWS Lambda function to transform data and ingest the transformed records into an Amazon Simple Storage Service (Amazon S3) bucket. In his spare time, he enjoys cycling with his new road bike.

Metrics 109

Metrics Visualization Dashboards Interactive 109

Cloudera

JUNE 18, 2022

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data Lake 89

Data Lake Data Warehouse Machine Learning Cost-Benefit 89

AWS Big Data

MARCH 2, 2023

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 data lakes and features like schema and partition evolution, time travel, and rollback.

Data Lake 121

Data Lake Data Processing Metadata Snapshot 121

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 107

Data Lake Sales Data Warehouse Snapshot 107

IBM Big Data Hub

APRIL 28, 2022

A data fabric can simplify data access in an organization to facilitate self-service data consumption, while remaining agnostic to data environments, processes, utility and geography. Obtaining access to each data warehouse and subsequently drawing relationships between the data would be a cumbersome process.

Metadata 81

Metadata Data Warehouse Forecasting Predictive Modeling 81

IBM Big Data Hub

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. ExampleCorp has multiple Redshift clusters.

Management 99

Management Data Warehouse Metrics Dashboards 99

Cloudera

JULY 14, 2023

Introduction For more than a decade now, the Hive table format has been a ubiquitous presence in the big data ecosystem, managing petabytes of data with remarkable efficiency and scale. They simply read the underlying data (not even full read, they just read the parquet headers) and create corresponding Iceberg metadata files.

Snapshot 69

Snapshot Metadata Data Warehouse Testing 69

AWS Big Data

MARCH 7, 2023

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning. Data lakes are more focused around storing and maintaining all the data in an organization in one place.

Analytics 73

Analytics Data Warehouse Data Lake Metadata 73

AWS Big Data

APRIL 25, 2024

Additionally, it enables cost optimization by aligning resources with specific use cases, making sure that expenses are well controlled. By isolating workloads with specific security requirements or compliance needs, organizations can maintain the highest levels of data privacy and security. secretsmanager ).

Metadata 108

Metadata Data Processing Management Testing 108

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

Risk 73

Risk Modeling Management Metadata 73

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Comprehensive search and access to relevant data.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Jet Global

JULY 2, 2019

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

datapine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle.

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

MARCH 7, 2024

Organizations often need to manage a high volume of data that is growing at an extraordinary rate. 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. Cold storage is optimized to store infrequently accessed or historical data.

Data Lake 117

Data Lake Analytics Dashboards Metrics 117

AWS Big Data

JANUARY 8, 2024

The destination can be an event-driven application for real-time dashboards, automatic decisions based on processed streaming data, real-time altering, and more. Using a data stream in the middle provides the advantage of using the time series data in other processes and solutions at the same time.

Analytics 116

Analytics IoT Data-driven Snapshot 116

AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

Data Lake 97

Data Lake Snapshot Big Data Data-driven 97

Cloudera

FEBRUARY 11, 2019

Cloudera provides a unified platform with multiple data apps and tools, big data management, hybrid cloud deployment flexibility, admin tools for platform provisioning and control, and a shared data experience for centralized security, governance, and metadata management.

Management 43

Management Metadata Analytics Machine Learning 43

AWS Big Data

JUNE 6, 2023

In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

Analytics 56

Analytics Data Lake Testing Optimization 56

AWS Big Data

MARCH 25, 2024

Streaming jobs constantly ingest new data to synchronize across systems and can perform enrichment, transformations, joins, and aggregations across windows of time more efficiently. Data streaming also helps you optimize data pipelines by processing only the change events, allowing you to respond to data changes more quickly and efficiently.

Data Lake 94

Data Lake Unstructured Data Management Modeling 94

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map.

Data Lake 97

Data Lake Data Warehouse Metadata Data Architecture 97

Cloudera

FEBRUARY 8, 2024

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. Cloudera Data Warehouse (CDW) running Hive has previously supported creating materialized views against Hive ACID source tables.

Snapshot 88

Snapshot Metadata Cost-Benefit Data Warehouse 88

AWS Big Data

FEBRUARY 1, 2023

This introduces the need for both polling and pushing the data to access and analyze in near-real time. From an operational standpoint, we designed a new shared responsibility model for data ingestion using AWS Glue instead of internal services (REST APIs) designed on Amazon EC2 to extract the data.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Sisense

MAY 28, 2020

Customers want to know that our platform will work seamlessly with their chosen cloud vendor, even if that just means something as basic-sounding as making sure queries between the vendor and Sisense are optimized.” That’s without mentioning outdated metadata—the data about data that provides data intelligence,” said Gopal.

Internet of Things 83

Internet of Things Metadata Data-driven Unstructured Data 83

AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. 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.

Data Lake 97

Data Lake Snapshot Metadata Optimization 97

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 104

Data Warehouse Data Lake Analytics Machine Learning 104

AWS Big Data

OCTOBER 4, 2023

An Amazon DataZone domain contains an associated business data catalog for search and discovery, a set of metadata definitions to decorate the data assets that are used for discovery purposes, and data projects with integrated analytics and ML tools for users and groups to consume and publish data assets.

Metadata 104

Metadata Data Lake Publishing Data Governance 104

AWS Big Data

NOVEMBER 15, 2023

BMW Group uses 4,500 AWS Cloud accounts across the entire organization but is faced with the challenge of reducing unnecessary costs, optimizing spend, and having a central place to monitor costs. The ultimate goal is to raise awareness of cloud efficiency and optimize cloud utilization in a cost-effective and sustainable manner.

Dashboards 98

Dashboards Analytics Metadata Data Warehouse 98

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

IBM Big Data Hub

OCTOBER 4, 2023

In fact, according in an IDC DataSphere study, IDC estimated that 10,628 exabytes (EB) of data was determined to be useful if analyzed, while only 5,063 exabytes (EB) of data (47.6%) was analyzed in 2022. How does an open data lakehouse architecture support AI? New insights and relationships are found in this combination.

Data Lake 81

Data Lake Metadata Cost-Benefit Data Warehouse 81

Cloudera

NOVEMBER 6, 2017

While cloud services do provide useful resources — such as compute instances and object storage on demand — Cloudera offers the unified platform to organize, process, analyze, and store data at large scale… anywhere. Security policies are set once and implemented uniformly upon data and users anywhere.

Metadata 40

Metadata Data Science Enterprise Risk 40

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 88

Enterprise Data Warehouse Data Lake Optimization 88

AWS Big Data

JANUARY 24, 2023

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

Data Architecture 68

Data Architecture Metadata Data Lake Snapshot 68

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 117

Data Lake Metadata Optimization Statistics 117

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 115

Data Lake Unstructured Data Data Warehouse Data Quality 115

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. Moreover, the framework should consume compute resources as optimally as possible per the size of the operational tables.

Data Lake 94

Data Lake Data Processing Metadata Snapshot 94

AWS Big Data

MARCH 26, 2024

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format. Let’s find out what role each of these components play in the context of C360.

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71