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Machine learning and data warehousing: What it is, why it matters

#artificialintelligence

One of the many technologies included under the umbrella of artificial intelligence, machine learning is defined by Wikipedia as "a field of computer science that gives computers the ability to learn without being explicitly programmed." The technology, which is a core part of the data analytics technologies that power the modern data warehouse, features algorithms that can make predictions on their own about data and its insights without being hampered by strict guidelines and instructions. When used successfully, machine learning can help with infrastructure scalability, cost savings, and agility. For its part, artificial intelligence (AI) is the ability of machines to think like humans. It stems from the idea that "given enough data and compute power, machines will be able to think and learn using mathematical simulation of the human brain," said John Santaferraro, research director at Enterprise Management Associates (EMA).


UNSW switches cloud-based data lakes for AI and ML capabilities

ZDNet

It was clear to the University of New South Wales (UNSW) that at the end of 2018, when it was developing its data strategy, it needed to improve the turnaround time it took to get information into the hands of decision makers. But to do that, the university had to set up a cloud-based data warehouse, which it opted to host in Microsoft Azure. The cloud-based warehouse now operates alongside the university's legacy data warehouse, which is currently hosted in Amazon Web Service's (AWS) EC2. "Our legacy data warehouse has been around for 10 to 15 years. But we started looking at what platforms can let us do everything that we do now, but also allows us to move seamlessly into new things like machine learning and AI," UNSW chief data and insights officer and senior lecture at the School of Computer Science and Engineering, Kate Carruthers said, speaking to ZDNet.


Python ETL Tools: Best 8 Options

#artificialintelligence

ETL is the process of fetching data from one or many systems and loading it into a target data warehouse after doing some intermediate transformations. The market has various ETL tools that can carry out this process. Some tools offer a complete end-to-end ETL implementation out of the box and some tools help you to create a custom ETL process from scratch and there are a few options that fall somewhere in between. In this post, we will see some commonly used Python ETL tools and understand in which situations they may be a good fit for your project. Before going through the list of Python ETL tools, let's first understand some essential features that any ETL tool should have.


Data Engineer Intern

#artificialintelligence

Aircall is on a mission to revolutionize the business phone industry! We are an advanced, cloud-based business phone system and call center software -- all wrapped up in one single tool (no hardware, 100% integrated). But behind our product are the people driving it . Ambition, Community, Teamwork and Transparency – these are the values we live by at Aircall. We know that success comes from smart work and deserves to be recognized and rewarded If you love a good challenge, enjoy solving meaningful problems, and want to be a part of one of the fastest growing B2B startups -- then Aircall is the company you are looking for!


Azure Synapse Analytics: A progress report

ZDNet

Azure Synapse Analytics was first revealed by Microsoft in November of 2019, at its Ignite conference in Orlando, back when we still had live events. With just a few months to go until its first birthday, we thought it would make sense to take a look at various platform features that have recently released to general availability (GA) and public preview. We also learned of an interesting and just-announced partnership Microsoft and Qlik have built around Synapse and thought that worthy of exploration as well. We'll cover it all in this post. Today, Synapse is Microsoft's cloud data warehouse platform, and integrated data lake functionality is now in public preview.


2020 Data & Analytics Trends

#artificialintelligence

Now that data is the most transformative asset in business, it's essential to prepare for what lies ahead, and to adjust strategies accordingly in order to successfully face the business landscape of tomorrow. We have identified 10 trends happening in 2020 that will be catalysts and enablers for change, and they will drive companies to enhance capabilities to stay at the forefront of innovation. They will allow data to be consumed dynamically and in different ways, causing people to search and think of new ways to use data. Given Trends These trends are a must, and they require action now. It's apparent that legacy on-premises platforms have failed to make data accessible to all users.


The unmistakable impact of AI on agencies Federal News Network

#artificialintelligence

We are using machine learning to control situations where there are a lot of variables. Data democratization means everyone has access to these data and tools. There are a ton of great tools out there that help folks who maybe aren't data scientists, but are data science-y and make better decisions at work. The growth of artificial intelligence and machine learning over the last few years is unmistakable. Agencies have realized the potential and real benefits of using the advanced technologies to improve decision making, analyze large databases and address mission challenges.


SodaStream deploys RPA, data warehouse, AI to streamline operations

#artificialintelligence

SodaStream, an Israeli manufacturer of fizzy drink devices, gained visibility in the U.S. and Europe as a healthy and environment friendly alternative to carbonated giants like Coca Cola. But soon after relocating from a controversial site in the occupied West Bank to a new facility in southern Israel, executives realised that the company is facing a new challenge: streamlining operations in order to stay competitive with low-cost manufacturer rivals from China while quenching a fast-growing thirst for its bubbly beverages. To rein in costs and make SodaStream's four manufacturing lines more efficient, executives decided to automate assembly lines with robots, computerise production, and connect all manufacturing processes under one control system. The multi-year project was aimed at boosting output to keep pace with 30 percent yearly sales surges, while utilising artificial intelligence, machine learning and cloud computing to get a better handle on optimising production. "We continued to grow rapidly and were packed with endless employees. The dining room was full. The production side was full. We knew that we wouldn't be able to allow ourselves to keep operating the same way… whether in terms of space, efficiency, or in terms of costs," said Kfir Suissa, chief operation officer at SodaStream, which was acquired by PepsiCo in 2018 for US$3.2 billion.


Multi-dimensional Time Series Analysis VS OLAP iunera

#artificialintelligence

Multi-dimensional Time Series Analysis and OLAP methods are important, when working with Time Series Data. Often multi-dimensional Time Series Analysis as term is referred to is a complete set of methods in applying machine learning in forms of forecasts or searching for anomalies and patterns. In this article we focus on good old deterministic multi-dimensional Time Series Analysis foundations to prepare, investigate and aggregate the Time Series Data in a deterministic way. Knowing these multi-dimensional Time Series Analysis foundations is essential, because at least 80% of Data Science work is Big Data and Big Data Landscape preparation. Common multi-dimensional analysis operations get applied in Business Intelligence and Data Warehousing where they are often called Online AnaLytical Processing (OLAP) operations [1]. In this article, we discuss and describe what the most important multi-dimensional Time Series Analysis and OLAP methods are and show examples of how the different operations are applied on a Time Series Data sets. In the beginning, we talk about OLAP in Data Warehouse landscapes and Time Series Data processing in Big Data landscapes. Subsequently, we give some insights into why and to whom multi-dimensional time series analysis with OLAP matters within an enterprise.


Ensuring Competitive Success Through Data and Insights Analytics Insight

#artificialintelligence

Over two quintillion bytes, once an incomprehensible amount, of data is being generated by the world each day. The art of understanding the data and filtering the noise from the essence and utilizing it to deliver value to businesses is on high demand. Modern digitization is fueling a radical shift in the playbook of every company, keeping data at the heart of this movement. With 65% of high performing companies leveraging data to enhance sectors such as customer acquisition, internal efficiency, product roadmaps, or pricing strategies, it is clear that utilizing data is an essential move for any company. But with the blinding amount of data available to companies, and the multitude of new technologies, it becomes difficult to discern where to start in the vast data and analytics landscape.