Watch this video to learn why economics play a major role in big data.
There are a lot of ways to look at big data and the modern data platforms that support it. Of course, a common perspective is the three Vs: volume, variety, and velocity. In this video, Matt Aslett looks at big data platforms from the vantage point of economics. He notes, for example, that over time the cost of keeping information has become less than the cost -- or risk -- of getting rid of it. The economics of storing vast quantities of data have changed so dramatically that it’s triggered the “fourth industrial revolution.”
In this video, you’ll learn about the contribution companies like Google have made to the development of modern data platforms.
Did you ever consider that you live in a world where Google -- and by extension other internet giants -- are sending you messages from the future? There’s no doubting the impact research from the likes of Google, Amazon, Facebook and other internet giants have had on the evolution of modern data platforms. These organizations wanted to do things with data that just couldn’t be accomplished with legacy data platforms. So… they created their own. 451 Research projects revenue from Hadoop and NoSQL to grow from $1B to $5B each by 2020, while it expects traditional relational and nonrelational database revenue to only grow by around 10 percent.
This video explains the importance of variety among modern data platforms.
When comparing modern data platforms with legacy relational databases, it’s useful to think in terms of schema. Traditional analytic databases, which are schema-on-write, are great for answering questions that the data was modeled to answer -- questions you knew you wanted to ask. But when the data, queries, or applications change, newer data platforms like Hadoop and NoSQL offer more flexibility. In fact, Hadoop and NoSQL, which are schema-on-read, work very well together. Their combination of high volume storage and high performance make them ideal for interactive applications.
From this video, you’ll learn how organizations are getting from data to insight.
Modern data platforms have lowered the cost of storing and processing data, which means organizations can afford to keep more data and more varieties of data -- including unstructured data. Of course, now they need to do something with it. From data to knowledge to actionable insight is the path to wisdom. For querying large volumes of data, SQL on Hadoop has emerged, along with technologies like Spark that has moved Hadoop beyond batch processing. Likewise, data lakes have become a way to create a unified repository that serves multiple analytic use cases, but they’ve increased concerns about data governance.
Watch this video to learn what modern data platforms allow organizations -- and the people within them -- to do that they couldn’t do before.
Data analysis has moved from static reporting to accessing various data sources by multiple users beyond domain experts and data scientists. In addition, organizations are developing or adopting data-driven applications with embedded analytics. Many of these operational applications rely on the technologies that support AI, such as machine learning and deep learning. In addition, rules and recommendation engines enable systems of engagement, such as customer support, that feature automated, data-driven decision-making, fundamentally changing the way people live and interact with technology.
In this video, you’ll see how analytic tools can handle SQL, NoSQL, and search-based data sources.
Most data-rich applications combine structured and unstructured data. A lot of valuable multi-structured data lives in non-relational data stores and search engines. In fact, by some estimates, as much as 80 percent of an organization’s data may reside outside of relational databases. So, when you're considering analytic tools to support your big data efforts, make sure they can connect to and analyze all types of data. There are a variety of methods analytic tools can use to query modern and legacy data sources.
Watch this video to learn about the challenge of blending traditional and modern data sets.
Today there are many sources of data. There’s structured data stored in relational databases. Unstructured data can live in Elasticsearch, Solr, or even social media. IoT devices like cars, household appliances, and smart phones produce a constant stream of real-time or near-real-time data. Then there are the modern data sources and platforms like Spark, Hadoop, Impala, and Kudu. The ability to blend this data is critical to extracting the maximum analytical value from it.
In this video, we take a look at the value of analyzing text data, especially in narrative forms like social media, clinical notes, and product reviews.
It’s easy to aggregate and store this information, but extracting knowledge from it requires special text analytics tools such as sentiment analysis, natural language processing, and computational linguistics. With these tools, we can assess things like the mood of a Tweet or the truthfulness of a product review. In a clinical setting, text analysis can add context to test results and other forms of quantitative medical data.
This video explains why the trend in modern analytics is to move computing power to the data and not the other way around.
In the past, we've had to move data from the source where it originated to the place where it would be processed and analyzed. For example, LambdaRail was a high-speed network designed for moving large data files over long distances. Grid FTP was developed for the same purpose. Sometimes moving data was as simple as getting in your car with a hard drive. Not anymore. Now we bring the compute power to where the data resides.
Watch this video to learn why it’s important for organizations to have the ability to run analytics on top of modern data sources.
Data schemas also have a role to play. Fixed schemas limit the range of queries you can make. Schemaless data supports fast queries on the fly. But for this, you need an abstraction layer so you can choose the most appropriate data source for the problem at hand. You also need to a way to deal with heterogeneous data sources. They’re just a fact of life in every organization.
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