What is Moore’s Law?
The observation made in 1965 by Gordon Moore,
co-founder of Intel, that the number of transistors per square inch on
integrated circuits had doubled every year since the integrated circuit was
invented. Moore predicted that this trend would continue for the foreseeable
future. In subsequent years, the pace slowed down a bit, but data density has
doubled approximately every 18 months, and this is the current definition of
Moore's Law, which Moore himself has blessed. Most experts, including Moore
himself, expect Moore's Law to hold for at least another two decades. The advent and evolution of cloud computing is
a testimonial to the law.
What is Cloud Computing?
Cloud
Computing provides a simple way to access servers, storage, databases and a
broad set of application services over the Internet. Cloud Computing providers
such as Amazon Web Services own and maintain the network-connected hardware
required for these application services, while you provision and use what you
need via a web application.
Let
us now delve into some of the latest developments pertaining to cloud computing
in the DW/BI sphere.
1. Amazon Redshift
Amazon
Redshift is a fast, fully managed, petabyte-scale data warehouse solution that
makes it simple and cost-effective to efficiently analyze all your data using
your existing business intelligence tools. Amazon Redshift’s data warehouse
architecture allows the user to automate most of the common administrative
tasks associated with provisioning, configuring and monitoring a cloud data
warehouse. Backups to Amazon S3 are continuous, incremental and automatic.
Restores are fast; you can start querying in minutes while your data is spooled
down in the background. Enabling disaster recovery across regions takes just a
few clicks.
Benefits of Amazon Redshift:
- Optimized for Data Warehousing: Amazon Redshift has a massively parallel processing (MPP) data warehouse architecture, parallelizing and distributing SQL operations to take advantage of all available resources.
- Scalable: With a few clicks of the AWS Management Console or a simple API call, you can easily change the number or type of nodes in your cloud data warehouse as your performance or capacity needs change.
- Fault Tolerant: Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.
2. Snowflake
Snowflake’s unique architecture takes full
advantage of all the cloud’s capabilities to store and process data. Scale up
and down at any time without costly redistribution of data, read-only downtime,
or hours of delay before new resources can be used. Based on a patent-pending
new architecture, Snowflake’s cloud service delivers the power of data
warehousing, the flexibility of big data platforms and the elasticity of the
cloud—at a 90 percent lower cost than on-premises data warehouses.
Benefits of Snowflake Cloud Services:
•
Data warehousing as a service. Snowflake eliminates the pains associated with managing and tuning
a database. That enables self-service access to data so that analysts can focus
on getting value from data rather than on managing hardware and software.
•
Multidimensional elasticity. Unlike existing products, Snowflake’s elastic scaling technology
makes it possible to independently scale users, data and workloads, delivering
optimal performance at any scale. Elastic scaling makes it possible to simultaneously
load and query data because every user and workload can have exactly the
resources needed, without contention.
• Single service for all business
data. Snowflake brings native storage of
semi-structured data into a relational database that understands and fully
optimizes querying of that data. Analysts can query structured and
semi-structured data in a single system without compromise.
Customer Case study: Adobe implemented Snowflake’s Cloud based offering
because of the flexibility that came from separating compute from storage
provides users and applications with on-demand access to business-critical data
at the performance level and scale required. Adobe’s testing indicated that
Snowflake’s cost / performance ratio could exceed alternate cloud-based
solutions in the market.”
Top 5 Trends in Cloud Data Warehousing and Analytics for 2015
Given the urgency that today’s ever
increasing data volumes and complexity levels present, organizations are
searching for ways to keep the focus on their business rather than their IT
infrastructure. Advances in cloud-based infrastructure and technology are
leading companies to trust more of their critical functions to the cloud,
including large scale cloud-based data marts and data warehouses.
- Trend #2: Increased Enablement of Self-Service Data Access via Cloud data Integration Services
Even the most mature analytics organizations
struggle with the gap between business analysts who need access to information
that is not in existing systems and actually making that information
accessible. Developers on the IT side work to create applications to house and
maintain this data, but these solutions are often disparate and have no
governance from or integration to a data warehouse or one another. New
cloud-based data integration and data refinery technologies can allow
organizations to close this gap by providing APIs to easily move data between
cloud data stores.
- Trend #3: Continued Growth of NoSQL Adoption
NoSQL databases showed a 7% increase in
adoption in 2014[1], with reasons for increased interest ranging from faster
and more flexible development to lower deployment costs. NoSQL databases not
only offer a low-risk, low-cost solution for organizations looking to get
started with cloud-based analytics but also provide one of the most efficient,
scalable solutions for cloud data storage as well. Additionally, new types of
NoSQL tools, such as graph databases for analyzing relationship networks and
key-value pair databases for data stream analysis, are gaining popularity for
specific analytic use cases.
- Trend #4: Big Data Analytics in the Cloud
Big data represents a major focal point for
many organizations in recent years. The challenge with big data analytics has
always been bringing the data to the analytics tool. Now, with new technologies
available for analyzing these data sets in the cloud, organizations are taking
advantage of the increased scalability and lower overhead, and we are seeing a
shift from physical machines to cloud-based big data solutions.
- Trend #5: Cloud-Based Analytics and Data Discovery
Deploying cloud-based analytics and data
discovery tools may be one of the simplest, most efficient ways for
organizations to engage their users and provide self-service Business
Intelligence capabilities to put the data in the hands of the business users
who can get the most insight from it.
http://aws.amazon.com/redshift/
https://www.ironsidegroup.com/2015/02/02/top-5-trends-in-cloud-data-warehousing-and-analytics-for-2015/
http://www.webopedia.com/TERM/M/Moores_Law.html
References:
http://www.snowflake.net/product/architecture/http://aws.amazon.com/redshift/
https://www.ironsidegroup.com/2015/02/02/top-5-trends-in-cloud-data-warehousing-and-analytics-for-2015/
http://www.webopedia.com/TERM/M/Moores_Law.html
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