data-warehouse 
The Crazy Programmer
MAY 12, 2021
Companies everywhere are handling more data than ever and all these terabytes of data need to be stored somewhere. Should you store the data in a database, a data warehouse, or a data lake? Choosing the right data storage solution will depend greatly on how the data is going to be used.
TechCrunch
DECEMBER 16, 2020
Hightouch , a SaaS service that helps businesses sync their customer data across sales and marketing tools, is coming out of stealth and announcing a $2.1 At its core, Hightouch, which participated in Y Combinator’s Summer 2019 batch, aims to solve the customer data integration problems that many businesses today face.
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Confluent
MARCH 12, 2024
Leverage Confluent Data Streaming Platform to bring real time to your smart warehouse, powering greater IoT automation, efficiency and cost savings.
TechCrunch
JANUARY 26, 2022
Israeli startup Firebolt has been taking on Google’s BigQuery, Snowflake and others with a cloud data warehouse solution that it claims can run analytics on large datasets cheaper and faster than its competitors. Big data is at the heart of how a lot of applications, and a lot of business overall, works these days.
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Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital.
Data Virtualization
OCTOBER 18, 2022
The post The Data Warehouse is Dead, Long Live the Data Warehouse, Part I appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information. In times of potentially troublesome change, the apparent paradox and inner poetry of these.
TechCrunch
DECEMBER 9, 2020
that was building what it dubbed an “operating system” for data warehouses, has been quietly acquired by Google’s Google Cloud division. Dataform scores $2M to build an ‘operating system’ for data warehouses. Dataform, a startup in the U.K.
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From inefficient queries to oversized warehouses to unexpected data growth, these cost issues often happen gradually beneath the surface. But their collective impact can add up to a budget-busting blizzard if not addressed.
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Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.
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An organization’s data is copied for many reasons, namely ingesting datasets into data warehouses, creating performance-optimized copies, and building BI extracts for analysis. Read this whitepaper to learn: Why organizations frequently end up with unnecessary data copies.
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In an effort to be data-driven, many organizations are looking to democratize data. However, they often struggle with increasingly larger data volumes, reverting back to bottlenecking data access to manage large numbers of data engineering requests and rising data warehousing costs.
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Data fuels the modern enterprise — today more than ever, businesses compete on their ability to turn big data into essential business insights. Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data.
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Enterprises are pouring money into data management software – to the tune of $73 billion in 2020 – but are seeing very little return on their data investments.
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From inefficient queries to oversized warehouses to unexpected data growth, these cost issues often happen gradually beneath the surface. But their collective impact can add up to a budget-busting blizzard if not addressed.
Speaker: Rob De Feo, Startup Advocate at Amazon Web Services
Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. So how can a startup harness machine learning for its own set of unique problems and solutions, and does it require a warehouse filled with PhDs to pull it off?
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