CIO Business Intelligence

JUNE 9, 2022

All this data arrives by the terabyte, and a data management platform can help marketers make sense of it all. Marketing-focused or not, DMPs excel at negotiating with a wide array of databases, data lakes, or data warehouses, ingesting their streams of data and then cleaning, sorting, and unifying the information therein.

Management 144

Management Advertising Data Lake Sales 144

AWS Big Data

APRIL 25, 2024

As the queries finish running, an UNLOAD operation is invoked from the Redshift data warehouse to the S3 bucket in Account A. Cross-account access has been set up between S3 buckets in Account A with resources in Account B to be able to load and unload data. If the alternate backend contains the needed value, it is returned.

Metadata 131

Metadata Data Processing Management Testing 131

Octopai

NOVEMBER 24, 2021

As the first of its reasons why to migrate to Redshift , Amazon says, “Amazon Redshift is fully managed and simple to use, enabling you to deploy a new data warehouse in minutes and load virtually any type of data from a range of cloud or on-premises data sources.”. Setting up the data warehouse can take minutes.

Data Warehouse 96

Data Warehouse Metadata Data Processing Reporting 96

AWS Big Data

MARCH 10, 2023

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. Data can be organized into three different zones, as shown in the following figure.

Data Lake 122

Data Lake Sales Data Warehouse Snapshot 122

erwin

APRIL 2, 2021

And knowing the business purpose translates into actively governing personal data against potential privacy and security violations. Do You Know Where Your Sensitive Data Is? Data is a valuable asset used to operate, manage and grow a business.

Data Governance 106

Data Governance Cost-Benefit Risk Metadata 106

AWS Big Data

MARCH 29, 2024

Data Firehose uses an AWS Lambda function to transform data and ingest the transformed records into an Amazon Simple Storage Service (Amazon S3) bucket. An AWS Glue crawler scans data on the S3 bucket and populates table metadata on the AWS Glue Data Catalog.

Metrics 128

Metrics Visualization Dashboards Interactive 128

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

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Less data gets decompressed, deserialized, loaded into memory, run through the processing, etc.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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 94

Data Strategy Strategy Data Warehouse Prescriptive Analytics 94

Cloudera

JANUARY 19, 2024

Supported AI models and services The SQL AI Assistant is not bundled with a specific LLM; instead it supports various LLMs and hosting services. The model can run locally, be hosted on CML infra or in the infrastructure of a trusted service provider. Log in to the Cloudera Data Warehouse service as DWAdmin.

Data Warehouse 77

Data Warehouse Data Processing Optimization Modeling 77

Cloudera

SEPTEMBER 29, 2022

This team or domain expert will be responsible for the data produced by the team. The data itself is then treated as a product. The data product is not just the data itself, but a bunch of metadata that surrounds it — the simple stuff like schema is a given. Data fabric defined. What is a data mesh contract?

Data Architecture 80

Data Architecture Data Warehouse Metadata Sales 80

AWS Big Data

JULY 20, 2023

Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table.

Data Lake 98

Data Lake Metadata Data Processing Big Data 98

AWS Big Data

DECEMBER 10, 2024

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. With its massively parallel processing (MPP) architecture and columnar data storage, Amazon Redshift delivers high price-performance for complex analytical queries against large datasets.

Sales 105

Sales Metadata Enterprise Testing 105

CIO Business Intelligence

SEPTEMBER 22, 2023

All this data arrives by the terabyte, and a data management platform can help marketers make sense of it all. DMPs excel at negotiating with a wide array of databases, data lakes, or data warehouses, ingesting their streams of data and then cleaning, sorting, and unifying the information therein.

Management 105

Management Advertising Data Lake Sales 105

AWS Big Data

MARCH 7, 2024

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 data lakes can become equally challenging.

Data Lake 131

Data Lake Analytics Dashboards Metrics 131

AWS Big Data

OCTOBER 18, 2023

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Spark SQL is an Apache Spark module for structured data processing. host') export PASSWORD=$(aws secretsmanager get-secret-value --secret-id $secret_name --query SecretString --output text | jq -r '.password')

Big Data 104

Big Data Data Processing Interactive Testing 104

Cloudera

MAY 12, 2021

2020 saw us hosting our first ever fully digital Data Impact Awards ceremony, and it certainly was one of the highlights of our year. We saw a record number of entries and incredible examples of how customers were using Cloudera’s platform and services to unlock the power of data. SECURITY AND GOVERNANCE LEADERSHIP.

Digital Transformation 72

Digital Transformation Machine Learning Optimization Data Lake 72

AWS Big Data

MARCH 9, 2023

Thousands of customers rely on Amazon Redshift to build data warehouses to accelerate time to insights with fast, simple, and secure analytics at scale and analyze data from terabytes to petabytes by running complex analytical queries. Data loading is one of the key aspects of maintaining a data warehouse.

Slice and Dice 98

Slice and Dice Data Warehouse Metrics Metadata 98

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Increasingly, these were simply web servers.

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84

IBM Big Data Hub

JULY 17, 2023

It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud. Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent. Later this year, it will leverage watsonx.ai

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Science Data Transformation 52

Cloudera

AUGUST 21, 2020

There are now tens of thousands of instances of these Big Data platforms running in production around the world today, and the number is increasing every year. Many of them are increasingly deployed outside of traditional data centers in hosted, “cloud” environments. But the “elephant in the room” is NOT ‘Hadoop’.

Cost-Benefit 77

Cost-Benefit Big Data ROI Risk 77

Alation

APRIL 14, 2023

In “The modern data stack is dead, long live the modern data stack!” the presenters elaborated on the common pain points of the cloud data warehouse today and predicted what it may look like in the future. Cloud costs are growing prohibitive.

Data Warehouse 52

Data Warehouse Data-driven Metadata Digital Transformation 52

AWS Big Data

MARCH 3, 2023

The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Athena supports reading native Delta tables and therefore we can read the data successfully even though the Data Catalog shows only a single array column.

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

Cloudera

NOVEMBER 13, 2017

The net result is much improved productivity for data engineers, data scientists, and analysts. Unified – Conceptually, cloud sounds like a single place to host diverse, data-intensive functions. The ability to discover and define metadata definitions for the business is a critical enabler for self-service functions.

Analytics 40

Analytics Big Data Machine Learning Cost-Benefit 40

Alation

FEBRUARY 8, 2022

Some argue that data governance and quality practices may vary between domains. Duplication of data, too, may become a problem, as siloed patterns emerge unique to the domains that host them. Principle #2: Data as a product. A data catalog is essential for several of these capabilities, according to Thoughtworks.

Data Governance 52

Data Governance Data-driven Metadata Enterprise 52

AWS Big Data

DECEMBER 4, 2024

These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products. The Institutional Data & AI platform adopts a federated approach to data while centralizing the metadata to facilitate simpler discovery and sharing of data products.

Metadata 104

Metadata Data Governance Data Quality Data-driven 104

AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 115

Data Lake Data Warehouse B2B Data-driven 115

Octopai

NOVEMBER 13, 2022

Data lineage is the ability to view the path of data as it flows from source to target within your data ecosystem, along with everything that happened to it along the way. And data lineage solutions will also show you any transformations the data underwent on its journey.

Enterprise 97

Enterprise Data Warehouse Reporting Metadata 97

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. public, private, hybrid cloud)?

Data Processing 100

Data Processing Data Warehouse Enterprise Visualization 100

Cloudera

MAY 18, 2021

CDP Public Cloud leverages the elastic nature of the cloud hosting model to align spend on Cloudera subscription (measured in Cloudera Consumption Units or CCUs) with actual usage of the platform. Experience configuration / use case deployment: At the data lifecycle experience level (e.g., Ongoing management.

Cost-Benefit 86

Cost-Benefit Data-driven Machine Learning Data Warehouse 86

Andrew White

JANUARY 11, 2021

On January 4th I had the pleasure of hosting a webinar. It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. See recorded webinars: Emerging Practices for a Data-driven Strategy. Link Data to Business Outcomes.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Laminar Security

MAY 3, 2023

When evaluating DSPM solutions , look for one that not only extends to all major cloud service providers, but also reads from various databases, data pipelines, object storage, disk storage, managed file storage, data warehouses, lakes, and analytics pipelines, both managed and self-hosted.

Management 40

Management Risk Risk Management Data Processing 40

Jet Global

MAY 1, 2023

These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. Metadata Self-service analysis is made easy with user-friendly naming conventions for tables and columns. addresses).

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

FEBRUARY 23, 2024

An on-premise solution provides a high level of control and customization as it is hosted and managed within the organization’s physical infrastructure, but it can be expensive to set up and maintain. This includes cleaning, aggregating, enriching, and restructuring data to fit the desired format.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52