Data Integration


Artificial Intelligence set to change the data storage landscape by 2025 Digit.in

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The report, called Data Age 2025 is sponsored by Seagate is all about the state of the global datasphere by the year 2025. However, IDC's report notes that there is a potential for automated data tagging using AI itself. Data integration tools and systems are now building cognitive/AI capabilities in them to help automate the process of data tagging using various types of machine learning, including supervised, unsupervised, and reinforcement learning. By sponsoring Data Age 2025, Seagate aims to gain insight into what the future may hold and create optimised solutions that tackle the data requirements of the future.


Data Integration Tools – Market Study

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This post is a brief review of leading Data Integration tools in the market. Heavily referencing from the Gartner 2016 report and peer reviews from my circle. The data integration tool market was worth approximately $2.8 billion at the end of 2015, an increase of 10.5% from the end of 2014 [2016 Gartner Report – Data Integration Tools].


Hitch Hikers Guide to Data Transformation on American Community Survey Using R

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Let's see how many rows and columns our data frame contains The data has 567 number of columns which means we have lots of variables. Let's now load the metadata file which contains the column name description. If you look at original data file which is acs_data, you'd see that there's a column named Geography which is not copied in estimates data frame. We are not interested in all the states, we are only interested in top 5 states which has highest number of houses running on solar energy.


Data Virtualization: Unlocking Data for AI and Machine Learning

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Hybrid Execution allows you to "push" queries to a remote system, such as to SQL Server, and access the referential data. However, one can imagine a use case where lots of ETL processing happens in HDInsight clusters and the structured results are published to SQL Server for downstream consumption (for instance, by reporting tools). Note the linear increase in execution time with SQL Server only (blue line) versus when HDInsight is used with SQL Server to scale out the query execution (orange and grey lines). With much larger real-world datasets in SQL Server, which typically runs multiple queries competing for resources, more dramatic performance gains can be expected.


If Data is as Valuable as Gold, It's Time to Polish Your Data Architecture

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Previous methods of gaining insight from data include ETL (Extract, Transform, Load) which involved copying the data and loading it into a data warehouse. The ETL process involves making copies of the data and then physically moving this copy and loading it into the data warehouse. The time is takes to extract, clean and load into the data warehouse not only takes a long time, it also requires many hands on deck. The need for the faster delivery of data insights requires a technology which is advanced enough to be able to integrate and gain value from heterogeneous sources; agile enough to be able to accommodate changes to business processes without affecting the architecture and fast enough to provide solutions in real-time.


SQL: optimizing or eliminating joins?

@machinelearnbot

The best approach would be one (1) SQL statement delivering it all in once. With this segregation a good well performing analytics environment has become very difficult. The DBMS is often normalized and/or oriented to server as destination of an ETL process delivering Cubes. Yes some good performance designs are possible with a DBMS with many joins/views .


Simplifying Data Pipelines in Hadoop: Overcoming the Growing Pains

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As an example, in order to execute a Hive query, an ETL engineer would only need to provide the SQL query, rather than writing a shell script containing Hive credentials and Hive commands, in addition to the SQL query that has to be executed. ETL workflow configuration file--ETL workflow configuration files contain workflows defined by a list of steps that should be executed in order to run an ETL process. ETL step artifacts--ETL step artifacts are files containing SQL statements, one liner shell/Python/sed scripts, or sometimes custom written executables. It, then, executes an ETL workflow defined in the ETL workflow configuration file, one step at a time using the runtime environment configuration file variables, as well as ETL runtime variables.


How to classify or model this problem?

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I have a data integration problem between two data sources (lets call them A and B), I have applied three functions(for the three attributes of every instance) in order to calculate the similarity between two instances a and b of each data source. I have in addition three sets that have the same form from above: the valid correspondences (user validation), invalid (again the user says so) and not yet classified (examples in the wild). Now, I want to calculate the optimal values for w1, w2 and w3 for maximize the value of PS when the correspondence is valid and at the same time reduce its value when the correspondence is invalid. So, after that I will use those values of w1,w2 and w3 in the not yet classified set and know if an entity is or is not a valid correspondence.


Putin, Merkel and Hollande Discuss Anti-Terrorism Data Exchange: Kremlin

U.S. News

MOSCOW (Reuters) - The leaders of Russia, Germany and France agreed in a phone call on Tuesday to speed up the exchange of data aimed at fighting terrorism, the Kremlin said. The Kremlin said the leaders also discussed the situation in Ukraine and the Easter ceasefire declared from April 1. President Donald Trump and Vladimir Putin discussed the attack on Monday. The annual event featured a few kites with a clear message to President Donald Trump.


Data Integration Tools – Market Study

@machinelearnbot

This post is a brief review of leading Data Integration tools in the market. Heavily referencing from the Gartner 2016 report and peer reviews from my circle. The data integration tool market was worth approximately $2.8 billion at the end of 2015, an increase of 10.5% from the end of 2014 [2016 Gartner Report – Data Integration Tools].