Published By: Jeppesen
Published Date: Oct 08, 2019
Get this infographic to go behind the scenes and explore the four phases of transforming raw aeronautical information into NavData. From Ingest to Completed Chart, the process ensures fidelity to the highest standard of quality and accuracy, resulting in four times few alerts issued than the next leading solution.
The stakes are high in today's data centers. Organisations have access to massive quantities of data promising valuable insights and new opportunities for business. But data center architects need to rethink and redesign their system architectures to ingest, store and process all that information. Similarly, application owners need to assess how they can process data more effectively. Those who don't re-architect might find themselves scrambling just to keep from being drowned in a data deluge.
Today’s leading DMPs are ingesting a wide range of owned and licensed data streams for insights and segmentation and are pushing data into a growing number of external targeting platforms, helping marketers deliver more relevant and consistent marketing communications.
This solution brief introduces the Smart Archive strategy from IBM, which is a comprehensive approach that combines IBM software, systems and service capabilities to help you drive down costs down and help ensure critical content is properly retained and protected.
For thousands of organizations, Splunk® has become mission-critical. But it’s still a very demanding workload. Pure Storage solutions dramatically improve Splunk Enterprise deployments by accelerating data ingest, indexing, search, and reporting capabilities – giving businesses the speed and intelligence to make faster, more informed decisions.
Published By: Attunity
Published Date: Jan 14, 2019
This whitepaper explores how to automate your data lake pipeline to address common challenges including how to prevent data lakes from devolving into useless data swamps and how to deliver analytics-ready data via automation.
Read Increase Data Lake ROI with Streaming Data Pipelines to learn about:
• Common data lake origins and challenges including integrating diverse data from multiple data source platforms, including lakes on premises and in the cloud.
• Delivering real-time integration, with change data capture (CDC) technology that integrates live transactions with the data lake.
• Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assemble a historical data store.
• Leveraging a scalable and autonomous streaming data pipeline to deliver analytics-ready data sets for better business insights.
Read this Attunity whitepaper now to get ahead on your data lake strategy in 2019.
In today’s world, it’s critical to have infrastructure that supports
both massive data ingest and rapid analytics evolution. At Pure
Storage, we built the ultimate data hub for AI, engineered to
accelerate every stage of the data pipeline.
Download this infographic for more information.
ESG Lab performed hands-on evaluation and testing of the Hitachi Content Platform portfolio, consisting of Hitachi Content Platform (HCP), Hitachi Content Platform Anywhere (HCP Anywhere) online file sharing, Hitachi Data Ingestor (HDI), and Hitachi Content Intelligence (HCI) data aggregation and analysis. Testing focused on integration of the platforms, global access to content, public and private cloud tiering, data quality and analysis, and the ease of deployment and management of the solution.
Any organization wishing to process big data from newly identified data sources, needs to first determine the characteristics of the data and then define the requirements that need to be met to be able to ingest, profile, clean,transform and integrate this data to ready it for analysis. Having done that, it may well be the case that existing tools may not cater for the data variety, data volume and data velocity that these new data sources bring. If this occurs then clearly new technology will need to be considered to meet the needs of the business going forward.
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes.
The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
Government agencies are taking advantage of new capabilities like mobile and cloud to deliver better services to its citizens. Many agencies are going paperless, streamlining how they interact with citizens and providing services more efficiently and faster. This short video will show real examples of how government agencies are applying new capabilities like cognitive and analytics to improve how they ingest, manage, store and interact with content.
Published By: StreamSets
Published Date: Sep 24, 2018
Treat data movement as a continuous, ever-changing operation and actively manage its performance.
Before big data and fast data, the challenge of data movement was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line.
Published By: Pentaho
Published Date: Mar 08, 2016
If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals.
Read the Buyer’s Guide to Big Data Integration by CITO Research to learn:
• What tools are most useful for working with Big Data, Hadoop, and existing transactional databases
• How to create an effective “data supply chain”
• How to succeed with complex data on-boarding using automation for more reliable data ingestion
• The best ways to connect, transport, and transform data for data exploration, analytics and compliance
A modern data warehouse is designed to
support rapid data growth and interactive analytics over a variety of relational, non-relational, and
streaming data types leveraging a single, easy-to-use interface. It provides a common architectural
platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling
organizations to derive deeper business insights.
Key elements of a modern data warehouse:
• Data ingestion: take advantage of relational, non-relational, and streaming data sources
• Federated querying: ability to run a query across heterogeneous sources of data
• Data consumption: support numerous types of analysis - ad-hoc exploration, predefined
reporting/dashboards, predictive and advanced analytics
Published By: BMC ASEAN
Published Date: Dec 18, 2018
Big data projects often entail moving data between multiple cloud and legacy on-premise environments. A typical scenario involves moving data from a cloud-based source to a cloud-based normalization application, to an on-premise system for consolidation with other data, and then through various cloud and on-premise applications that analyze the data. Processing and analysis turn the disparate data into business insights delivered though dashboards, reports, and data warehouses - often using cloud-based apps.
The workflows that take data from ingestion to delivery are highly complex and have numerous dependencies along the way. Speed, reliability, and scalability are crucial. So, although data scientists and engineers may do things manually during proof of concept, manual processes don't scale.
Published By: Dell EMC
Published Date: Mar 18, 2016
The EMC Isilon Scale-out Data Lake is an ideal platform for multi-protocol ingest of data. This is a crucial function in Big Data environments, in which it is necessary to quickly and reliably ingest data into the Data Lake using protocols closest to the workload generating the data. With OneFS it is possible to ingest data via NFSv3, NFSv4, SMB2.0, SMB3.0 as well as via HDFS. This makes the platform very friendly for complex Big Data workflows.
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights.
Key elements of a modern data warehouse:
• Data ingestion: take advantage of relational, non-relational, and streaming data sources
• Federated q
Pairing Apache Kafka with a Real-Time Database Learn how to:
? Scope data pipelines all the way from ingest to applications and analytics
? Build data pipelines using a new SQL command: CREATE PIPELINE ? Achieve exactly-once semantics with native pipelines
? Overcome top challenges of real-time data management
Published By: Snowflake
Published Date: Jan 25, 2018
To thrive in today’s world of data, knowing how to manage and derive value from of semi-structured data like JSON is crucial to delivering valuable insight to your organization. One of the key differentiators in Snowflake is the ability to natively ingest semi-structured data such as JSON, store it efficiently and then access it quickly using simple extensions to standard SQL.
This eBook will give you a modern approach to produce analytics from JSON data using SQL, easily and affordably.
Siloed data sources, duplicate entries, data breach risk—how can you scale data quality for ingestion and transformation at big data volumes?
Data and analytics capabilities are firmly at the top of CEOs’ investment priorities. Whether you need to make the case for data quality to your c-level or you are responsible for implementing it, the Definitive Guide to Data Quality can help.
Download the Definitive Guide to learn how to:
Stop bad data before it enters your system
Create systems and workflow to manage clean data ingestion and transformation at scale
Make the case for the right data quality tools for business insight
Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now.
Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data can now be processed in memory,
and more significantly, analyzed as it arrives, in real time. Millions to hundreds of millions of events (such as video streams or application alerts) can be collected and analyzed per hour to deliver insights that can be acted upon in an instant. From financial services to manufacturing, this rev
Read this Solution Guide to learn how to optimize monitoring & security for 40G & 100G networks
While network owners are migrating to 40G & 100G infrastructures, most tools available today cannot ingest high-speed traffic. They lack both the physical interfaces and processing power to do so.
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for
the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and
discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments
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