AWS Big Data

JULY 20, 2023

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. Why did Orca build a data lake?

Data Lake 94

Data Lake Analytics Snapshot Data Quality 94

AWS Big Data

DECEMBER 4, 2024

Amazon SageMaker Lakehouse , now generally available, unifies all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. The tools to transform your business are here.

Data Analytics 86

Data Analytics Analytics Data Lake Data Quality 86

CDW Research Hub

APRIL 18, 2022

The complexity and cost of SIEM solutions and the number of resources that security consumes can easily swallow a large portion of an enterprise’s budget, causing many organizations to fall behind in the security data race. Security data lakes can reduce organizations’ reliance on SIEM solutions.

Data Lake 75

Data Lake Data Collection Enterprise Risk 75

Jet Global

JULY 2, 2019

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 data lake. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

IBM Big Data Hub

MAY 2, 2018

Making your data lake a “governed data lake” is the game changer. Without governance, organizations risk securing the data and as well as protecting it. A governed data lake contains data that’s accessible, clean, trusted and protected.

Data Lake 65

Data Lake Data Quality Risk IT 65

CIO Business Intelligence

SEPTEMBER 20, 2023

The data can also help us enrich our commodity products. How are you populating your data lake? We’ve decided to take a practical approach, led by Kyle Benning, who runs our data function. Then our analytics team, an IT group, makes sure we build the data lake in the right sequence.

Data Lake 135

Data Lake Data-driven Dashboards Risk 135

AWS Big Data

JANUARY 9, 2025

Unlike direct Amazon S3 access, Iceberg supports these operations on petabyte-scale data lakes without requiring complex custom code. Icebergs table format separates data files from metadata files, enabling efficient data modifications without full dataset rewrites. At petabyte scale, Icebergs advantages become clear.

Metadata 107

Metadata Snapshot Cost-Benefit Optimization 107

CIO Business Intelligence

OCTOBER 9, 2024

However, this enthusiasm may be tempered by a host of challenges and risks stemming from scaling GenAI. As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls. That’s why many enterprises are adopting a two-pronged approach to GenAI.

Strategy 143

Strategy Modeling Management Data Lake 143

CIO Business Intelligence

APRIL 8, 2025

Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. Data quality is no longer a back-office concern. The decisions you make, the strategies you implement and the growth of your organizations are all at risk if data quality is not addressed urgently.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

CIO Business Intelligence

MARCH 9, 2022

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, data lake convergence. Meet the data lakehouse.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

Alation

FEBRUARY 20, 2020

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. Due to these needs, hybrid cloud data lakes emerged as a logical middle ground between the two consumption models. earthquake, flood, or fire), where the data collected does not need to be as tightly controlled.

Data Lake 52

Data Lake ROI Metadata Cost-Benefit 52

AWS Big Data

JULY 29, 2024

In the era of big data, data lakes 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.

Metadata 119

Metadata Snapshot Data Lake Metrics 119

CIO Business Intelligence

MAY 21, 2024

It will serve as the “nerve center” of an enterprise’s IT operation, the company said, adding that the offering will generate insights across an enterprise’s folio of applications to help reduce risk and compliance processes.

Enterprise 110

Enterprise Management Data Lake Risk 110

erwin

SEPTEMBER 3, 2020

You can collect complete application ecosystem information; objectively identify connections/interfaces between applications, using data; provide accurate compliance assessments; and quickly identify security risks and other issues. You can better manage risk because of real-time data coming into the EA space.

Data Governance 144

Data Governance Enterprise Risk Data Lake 144

Corinium

JUNE 6, 2019

Lack of clear, unified, and scaled data engineering expertise to enable the power of AI at enterprise scale. Regulations and compliance requirements, especially around pricing, risk selection, etc., Progressing AI based solutions from proof of concept or minimum viable product (MVP) to production.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 113

Data Lake Data Warehouse Management Risk 113

IBM Big Data Hub

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 70

Risk Modeling Management Metadata 70

erwin

AUGUST 15, 2022

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. This blog is an introduction to some advanced NoSQL and data lake database design techniques (while avoiding common pitfalls) is noteworthy. Data Modeling.

Data Lake 52

Data Lake Modeling Unstructured Data Data Warehouse 52

erwin

OCTOBER 31, 2019

While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed. So being prepared means you can minimize your risk exposure and the damage to your reputation.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

CIO Business Intelligence

APRIL 29, 2022

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.

Data Governance 145

Data Governance IT Data Lake Risk 145

CIO Business Intelligence

AUGUST 9, 2024

The alternative to synthetic data is to manually anonymize and de-identify data sets, but this requires more time and effort and has a higher error rate. The European AI Act also talks about synthetic data, citing them as a possible measure to mitigate the risks associated with the use of personal data for training AI systems.

Risk 138

Risk Measurement Data Lake Data Collection 138

CIO Business Intelligence

JANUARY 20, 2023

Doing it right requires thoughtful data collection, careful selection of a data platform that allows holistic and secure access to the data, and training and empowering employees to have a data-first mindset. Security and compliance risks also loom. Most organizations don’t end up with data lakes, says Orlandini.

Data Lake 135

Data Lake Data-driven Finance Data Architecture 135

CIO Business Intelligence

SEPTEMBER 6, 2023

While the potential of Generative AI in software development is exciting, there are still risks and guardrails that need to be considered. Risks of AI in software development Despite Generative AI’s ability to make developers more efficient, it is not error free. To learn more, visit us here. Artificial Intelligence, Machine Learning

Software 119

Software Experimentation Risk Data Lake 119

CIO Business Intelligence

OCTOBER 25, 2023

Zscaler Enterprises will work to secure AI/ML applications to stay ahead of risk Our research also found that as enterprises adopt AI/ML tools, subsequent transactions undergo significant scrutiny. In all likelihood, we will see other industries take their lead to ensure that enterprises can minimize the risks associated with AI and ML tools.

Enterprise 133

Enterprise Manufacturing Risk Data-driven 133

AWS Big Data

JUNE 25, 2024

This post is co-authored by Vijay Gopalakrishnan, Director of Product, Salesforce Data Cloud. In today’s data-driven business landscape, organizations collect a wealth of data across various touch points and unify it in a central data warehouse or a data lake to deliver business insights.

Data Lake 112

Data Lake Cost-Benefit Data-driven Data Warehouse 112

DataKitchen

DECEMBER 6, 2022

The two things we are most excited about are: First, DataOps is distinct from all Data Analytic tools. As founders, we sat in a room eight years ago (when all the rage was Hadoop, data prep, and data lakes) and debated — will there ever be an ‘ops’ layer that sits next to all the current data tools?

Software 130

Software Marketing Data Lake Testing 130

AWS Big Data

NOVEMBER 29, 2023

With Redshift, we are able to view risk counterparts and data in near real time— instead of on an hourly basis. Zero-ETL integration also enables you to load and analyze data from multiple operational database clusters in a new or existing Amazon Redshift instance to derive holistic insights across many applications.

Data Warehouse 113

Data Warehouse Analytics Data Lake Machine Learning 113

Cloudera

AUGUST 13, 2021

Every enterprise is trying to collect and analyze data to get better insights into their business. Whether it is consuming log files, sensor metrics, and other unstructured data, most enterprises manage and deliver data to the data lake and leverage various applications like ETL tools, search engines, and databases for analysis.

Analytics 111

Analytics Data Lake Unstructured Data Data Strategy 111

Cloudera

JANUARY 21, 2021

And you also already know siloed data is costly, as that means it will be much tougher to derive novel insights from all of your data by joining data sets. Of course you don’t want to re-create the risks and costs of data silos your organization has spent the last decade trying to eliminate. Must you be: .

Data Warehouse 94

Data Warehouse Data Lake IT Analytics 94

erwin

OCTOBER 8, 2020

No less daunting, your next step is to re-point or even re-platform your data movement processes. And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative. The metadata-driven suite automatically finds, models, ingests, catalogs and governs cloud data assets.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

Cloudera

MAY 28, 2024

Modern data architectures deliver key functionality in terms of flexibility and scalability of data management. This form of architecture can handle data in all forms—structured, semi-structured, unstructured—blending capabilities from data warehouses and data lakes into data lakehouses.

Data Architecture 88

Data Architecture Data Lake Data Warehouse Cost-Benefit 88

AWS Big Data

MARCH 27, 2023

Data lakes have come a long way, and there’s been tremendous innovation in this space. Today’s modern data lakes are cloud native, work with multiple data types, and make this data easily available to diverse stakeholders across the business. In the navigation pane, under Data catalog , choose Settings.

Data Lake 92

Data Lake Data-driven Management Data Architecture 92

CIO Business Intelligence

OCTOBER 19, 2022

We kept the data warehouse but augmented it with a cloud-based enterprise data lake and ML platform. The core customer data stays pristine in the data warehouse, but once the data goes into the lake, the business functions can experiment. The CIO’s new remit regarding business risk Now I’ve got it.

IT 124

IT Data Warehouse Digital Transformation Risk 124

Cloudera

MAY 19, 2021

By leveraging the parallel compute capacity of GPUs the time for complicated data engineering and data science tasks can be dramatically reduced, accelerating the timeframes for Data Scientists to take ideas from concept to production. Data Ingestion. The raw data is in a series of CSV files.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

AWS Big Data

JANUARY 31, 2023

In addition, data governance is required to comply with an increasingly complex regulatory environment with data privacy (such as GDPR and CCPA) and data residency regulations (such as in the EU, Russia, and China). Sharing data using LF-tags helps scale permissions and reduces the admin work for data lake builders.

Data Lake 90

Data Lake Data Governance Data Architecture Machine Learning 90

AWS Big Data

DECEMBER 4, 2024

Globally, financial institutions have been experiencing similar issues, prompting a widespread reassessment of traditional data management approaches. With this approach, each node in ANZ maintains its divisional alignment and adherence to data risk and governance standards and policies to manage local data products and data assets.

Metadata 94

Metadata Data Governance Data Quality Data-driven 94

erwin

APRIL 2, 2021

With more companies increasingly migrating their data to the cloud to ensure availability and scalability, the risks associated with data management and protection also are growing. Data Security Starts with Data Governance. Lack of a solid data governance foundation increases the risk of data-security incidents.

Data Governance 106

Data Governance Cost-Benefit Metadata Risk 106