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Introducing Amazon MWAA larger environment sizes

AWS Big Data

xlarge 8 vCPUs / 24 GB 4 vCPUs / 12 GB 40 tasks (default) Up to 2000 mw1.2xlarge 16 vCPUs / 48 GB 8 vCPUs / 24 GB 80 tasks (default) Up to 4000 With the introduction of these larger environments, your Amazon Aurora metadata database will now use larger, memory-optimized instances powered by AWS Graviton2.

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Self-Serve Analytics Supports Continuous Improvement!

Smarten

When you provide access to augmented analytics solutions for your business users, you distribute knowledge and engender fact-based decision making, helping your users to accurately complete tasks and plan for future success. There is good reason for these results.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

In our example, we have configured a ruleset against a table containing patient data within a healthcare synthetic dataset generated using Synthea. Synthea is a synthetic patient generator that creates realistic patient data and associated medical records that can be used for testing healthcare software applications.

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Technical Analysis is Changing Quickly in the Era of Big Data

Smart Data Collective

As we pointed out in a previous article, you can even use data analytics to provide better Fibonacci retracements. This methodology is grounded in concrete, empirical evidence that has been tested and proven over time. By analyzing vast amounts of data, we unveil patterns and correlations that were previously hidden.

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Data Analytics Helps Marketers Substantially Boost Image SEO

Smart Data Collective

Data analytics technology has become a very important element of modern marketing. One of the ways that big data is transforming marketing is through SEO. We have previously talked about data-driven SEO. However, we feel that it is time to have a more nuanced discussion about using big data in SEO.

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Resolve private DNS hostnames for Amazon MSK Connect

AWS Big Data

NAME=Customer Name 2000,MKTSEGMENT=Market Segment 9},source=Struct{version=1.9.5.Final,connector=mysql,name=salesdb-server,ts_ms=1678099992174,snapshot=last,db=salesdb,table=CUSTOMER,server_id=0,file=binlog.000001,pos=43298383,row=0},op=r,ts_ms=1678099992174} NAME=Customer Name 2000,MKTSEGMENT=Market Segment 9},after=Struct{CUST_ID=2000.0,NAME=Customer

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Q&A with Greg Rahn – The changing Data Warehouse market

Cloudera

And then I moved from Madison, Wisconsin to San Francisco in 2000, to chase the dotcom dream. After having rebuilt their data warehouse, I decided to take a little bit more of a pointed role, and I joined Oracle as a database performance engineer. Let’s talk about big data and Apache Impala. Michael Moreno: Nice!