Alation

FEBRUARY 20, 2020

By changing the cost structure of collecting data, it increased the volume of data stored in every organization. Additionally, Hadoop removed the requirement to model or structure data when writing to a physical store. The result, as many industry observers have put it, is that many data lakes become data swamps.

Data Lake 52

Data Lake Metadata Structured Data Big Data 52

AWS Big Data

FEBRUARY 26, 2025

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 data lakes to empowering AI-driven analytics, AWS unveiled new tools and services that are set to shape the future of data and analytics.

Analytics 60

Analytics Data Lake Metadata Data Warehouse 60

AWS Big Data

JANUARY 12, 2024

Ingestion: Data lake batch, micro-batch, and streaming Many organizations land their source data into their data lake in various ways, including batch, micro-batch, and streaming jobs. Amazon AppFlow can be used to transfer data from different SaaS applications to a data lake.

Data Lake 115

Data Lake Cost-Benefit Visualization Structured Data 115

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

Data Governance 118

Data Governance Unstructured Data Metadata Data Lake 118

CIO Business Intelligence

OCTOBER 18, 2022

Noel had already established a relationship with consulting firm Resultant through a smaller data visualization project. Resultant recommended a new, on-prem data infrastructure, complete with data lakes to provide stake holders with a better way to manage data reliability, accuracy, and timeliness.

Data Transformation 141

Data Transformation Consulting Data Lake Reporting 141

Sisense

JULY 22, 2020

The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3 , Hadoop and Microsoft Azure. Sisense provides instant access to your cloud data warehouses.

Slice and Dice 98

Slice and Dice Digital Transformation Data Warehouse Data Lake 98

CIO Business Intelligence

OCTOBER 25, 2023

The release of intellectual property and non-public information Generative AI tools can make it easy for well-meaning users to leak sensitive and confidential data. Once shared, this data can be fed into the data lakes used to train large language models (LLMs) and can be discovered by other users.

Enterprise 133

Enterprise Manufacturing Risk Data-driven 133

AWS Big Data

JUNE 21, 2023

The challenge comes when we need to ask more complex questions of our data, for example, what was the year-on-year quarterly sales growth by product broken down by country? The case for a data warehouse A data warehouse is ideally suited to answer OLAP queries. To house our data, we need to define a data model.

Data Warehouse 99

Data Warehouse Data Lake OLAP Cost-Benefit 99

AWS Big Data

OCTOBER 25, 2023

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. By some estimates, unstructured data can make up to 80–90% of all new enterprise data and is growing many times faster than structured data.

Unstructured Data 132

Unstructured Data Metadata Management Analytics 132

AWS Big Data

MARCH 27, 2024

Amazon Redshift integrates with AWS HealthLake and data lakes through Redshift Spectrum and Amazon S3 auto-copy features, enabling you to query data directly from files on Amazon S3. This means you no longer have to create an external schema in Amazon Redshift to use the data lake tables cataloged in the Data Catalog.

Data Analytics 113

Data Analytics Analytics Data Warehouse Data Lake 113

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 87

Analytics Data Warehouse Data Lake Metadata 87

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

AWS Big Data

MARCH 25, 2024

Both engines provide native ingestion support from Kinesis Data Streams and Amazon MSK via a separate streaming pipeline to a data lake or data warehouse for analysis. OpenSearch Service offers visualization capabilities powered by OpenSearch Dashboards and Kibana (1.5

Data Lake 113

Data Lake Unstructured Data Management Snapshot 113

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

AWS Big Data

MARCH 26, 2024

Strategize based on how your teams explore data, run analyses, wrangle data for downstream requirements, and visualize data at different levels. The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud.

Data Strategy 85

Data Strategy Strategy Data Warehouse Prescriptive Analytics 85

Cloudera

JULY 26, 2021

A common pitfall in the development of data platforms is that they are built around the boundaries of point solutions and are constrained by the technological limitations (e.g., a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g., data warehousing).

Strategy 119

Strategy Data Science Unstructured Data Marketing 119

Sisense

APRIL 26, 2020

Structured vs unstructured data. Structured data is far easier for programs to understand, while unstructured data poses a greater challenge. However, both types of data play an important role in data analysis. Structured data. Structured data is organized in tabular format (ie.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development.

Metadata 59

Metadata Data Quality Statistics Data Science 59

CDW Research Hub

DECEMBER 4, 2019

Cloud-based data warehouses can also perform complex analytical queries much faster due to the use of massively parallel processing (MPP), which uses multiple processors—each with its own operating system and memory—to simultaneously perform a set of coordinated computations.

Data Warehouse 52

Data Warehouse Data mining Data Lake Dashboards 52

Cloudera

NOVEMBER 15, 2024

A Better Way Forward: Cloudera’s Open Data Lakehouse Cloudera offers a solution to these challenges with its open data lakehouse, which combines the flexibility and scalability of data lake storage with data warehouse functionality to unify and simplify the management of cyber log data.

Analytics 59

Analytics Metadata Snapshot Data-driven 59

AWS Big Data

APRIL 20, 2023

Customers use Amazon Redshift to run their business-critical analytics on petabytes of structured and semi-structured data. Apache Spark enables you to build applications in a variety of languages, such as Java, Scala, and Python, by accessing the data in your Amazon Redshift data warehouse.

Data Lake 93

Data Lake Data Warehouse Sales Data-driven 93

Sisense

MAY 11, 2021

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

FineReport

APRIL 11, 2023

Every user can now create interactive reports and utilize data visualization to disseminate knowledge to both internal and external stakeholders. A business intelligence dashboard, also known as a BI dashboard, is a tool that presents important business metrics and data points in a visual and analytical format on a single screen.

Dashboards 40

Dashboards Business Intelligence Metrics Cost-Benefit 40

AWS Big Data

JULY 28, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is designed for analyzing large volumes of data and performing complex queries on structured and semi-structured data. Data mapping involves identifying and documenting the flow of personal data in an organization.

Snapshot 96

Snapshot Metadata Measurement Data Warehouse 96

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly.

Enterprise 52

Enterprise Knowledge Discovery Risk Machine Learning 52

AWS Big Data

DECEMBER 21, 2023

To bring their customers the best deals and user experience, smava follows the modern data architecture principles with a data lake as a scalable, durable data store and purpose-built data stores for analytical processing and data consumption. This is the Data Mart stage.

Data Lake 111

Data Lake Data Warehouse Data-driven B2B 111

CIO Business Intelligence

MARCH 6, 2023

Advancements in analytics and AI as well as support for unstructured data in centralized data lakes are key benefits of doing business in the cloud, and Shutterstock is capitalizing on its cloud foundation, creating new revenue streams and business models using the cloud and data lakes as key components of its innovation platform.

Data Lake 88

Data Lake Cost-Benefit Recreation/Entertainment Unstructured Data 88

Cloudera

NOVEMBER 15, 2024

A Better Way Forward: Cloudera’s Open Data Lakehouse Cloudera offers a solution to these challenges with its open data lakehouse, which combines the flexibility and scalability of data lake storage with data warehouse functionality to unify and simplify the management of cyber log data.

Analytics 84

Analytics Metadata Snapshot Data-driven 84

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

BI-Survey

FEBRUARY 27, 2025

table-level) data lineage visualization? Advanced: Does it leverage AI/ML to enrich metadata by automatically linking glossary entries with data assets and performing semantic tagging? Basic: Does the catalog recognize and register unstructured data sources, such as data lakes or document storage systems?

Unstructured Data 52

Unstructured Data Metadata Data Quality Data Governance 52