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Data Warehouse Automation System Meaning and Examples

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Adeline Howell


Data warehouse automation is more complex than robots zooming around your company warehouse, completing tasks. Processes including, but not limited to, design, development, operations, impact analysis, and testing are involved in data warehouse automation systems.

What exactly is data warehouse automation? Does it differ from an understanding of warehouse automation? Let’s dive into what data warehouse automation is and how it’s different to understand than warehouse automation.

Definition

From a definitional state, data uses metrics from a digital form to produce growth or spark discussion. In a warehouse, data is vital to the survival of the company. Without data, there are no logistics, and it’s hard to optimize a warehouse. Data warehouse automation uses development to automate the lifecycle processes with the help of artificial intelligence (AI).

A warehouse lifecycle goes from a repetitive process to one that is much more automated and more user-friendly with data warehouse automation. This can help the warehouse automation tools such as robots and sensors

Data warehousing is the process of using metrics and other statistics to not only generate code but deploy code. By doing this, it is easier to develop the best design of the warehouse inside and out for the project at hand.

Storage in the database for a warehouse uses real-time data. Think of orders going in and out, or for a manufacturing line, new products going in and out, all in real-time, not stalled by delays in reporting. That database keeps up with those numbers so that humans don’t have to, but that is only the beginning. This not only eliminates errors but also helps with labor costs and frees up those working to help in more prominent parts of each project.

Examples of Data Warehouse Automation:

Used to streamline data computerization, data warehouse automation systems use extraction, transformation, and loading (ETL for short) tools to complete the job. The cost of these tools for projects ranges between 20K and 20M, depending on the complexity of the project and the tool you use.

Below are 7 of my favorite data warehouse automation tool systems, but there are plenty more where these came from.

Amazon Redshift

As a cloud-based analytical and BI tool, Amazon Redshift has a lot of useful functions, making it one of the more popular data warehouse automation tools. This service is easy to customize as well as easy to integrate with previous databases. The custom functionality of Amazon Redshift is limitless due to its storage, processing, and optimization. The service even has a two-month free trial to make sure you love it before taking the plunge into its data warehouse automation tool.

Oracle

Data-driven is at the forefront of Oracle, which is also a cloud-based tool. Machine learning analysis, auto-tuning, and data visualization are some of the biggest and most desirable features of Oracle. This tool is best for heavily analytical warehouses and projects. Oracle will generate reports and predictions based on your received data and help your company to grow through warehousing projects.

ActiveBatch

ActiveBatch uses end-to-end solutions to ensure a real-time database for users. It features advanced scheduling and a job library, which is helpful for automating designs and quickly moving through projects. You can add multiple checkpoints to increase usability. It also has a free demo and 30-day trial, which is useful for getting started with ActiveBatch.

Redwood RunMyJobs

Redwood is great for companies expecting growth. It’s user-friendly and has unparalleled scalability to be able to grow with you rather than needing to change your automated tools as you grow. It has incredible visibility features, great for keeping up with everything behind the scenes, and has integration capabilities for multiple sources to keep all your data together. It also has the intelligence to feed data onto dashboards and build reports. This can give even more time back to you as a manager, or employee, by not having to develop those reports and dashboards yourself.

Zap DataHub

Zap DataHub is extremely user-friendly. There is no coding involved in setup and use. It is wizard-based automation, meaning the interface itself will lead the user through the necessary steps for setup or any features. Also, Zap offers a free demo to get companies started, not to mention it is one of the lower-priced options for data warehouse automation tools on the market. Another key feature of Zap is the intuitive modeling it offers, meaning you can drag and drop within the interface.

WhereScape

Popular for its infrastructure automation, WhereScape automates designs and streamlines projects. Doing this can reduce the overall timeline for production. WhereScape focuses on design, development, deployment, and operation. It also has add-on services; WhereScape 3D, WhereScape® Red, and WhereScape® Data Vault Express.

Astera

Astera is an agile management automation tool that implements design patterns through its data automation software. They use a code-free design, making it extremely user-friendly similar to Zap DataHub. The biggest difference between the two comes down to Astera being metadata-driven, and Zap DataHub being wizard-based. You can also request a free trial for Astera before you decide to invest.

How to Know it’s Time

Warehouses will know that data needs an automation element to it when their processes are starting to check the wrong boxes. If you are finding that all of your or your employee’s time is being spent working on data warehousing projects, or your projects are taking much longer than anticipated due to the volume of data, it may be time to consider some of the above options to automate the process a bit. As you can see, there is a lot of flexibility and many options to choose from.

Wrap-Up

This blog has given you insider information into the world of data warehouse automation. Additionally, why it’s important to the overall structure of a warehouse automation system. You also gained knowledge on several popular data warehouse automation tools to help you whenever the time may come to use one for your own warehouse projects.

Featured Image Credit; Pexels; Thank you!

Adeline Howell

Adeline Howell is skilled in AP Writing, Critical Thinking, Microsoft Office, Social Media, and Teamwork, with a demonstrated history of working on projects relating to the Sports Marketing and Public Relations industries. Adeline has advanced leadership skills by coaching volleyball and a drive to learn each day with additional experience as a Warehouse Leader and Retail Sales Associate. Strong community and social services professional with a Bachelor’s degree focused in Communication and Public Relations from the University of North Carolina at Charlotte.

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Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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