Ryft Cloud ODBC/JDBC Connector

Transcription

1 Ryft Cloud ODBC/JDBC Connector SQL Reference Guide Ryft Document Number: 1208 Document Version: Release Date: October Ryft Systems, Inc. All Rights in this documentation are reserved. RYFT SYSTEMS, INC.

2 Copyright (c) 2017, Ryft Systems, Inc. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. All advertising materials mentioning features or use of this software must display the following acknowledgement: This product includes software developed by Ryft Systems, Inc. Neither the name of Ryft Systems, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY RYFT SYSTEMS, INC. ''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL RYFT SYSTEMS, INC. BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Ryft Systems, Inc. Page ii

3 Revision History Date Reason for Change Version October 2017 Initial Release Ryft Systems, Inc. Page iii

4 Contents Using This Guide... v Ryft Technical Support... v About Ryft Systems Inc.... v 1: ODBC/JDBC Connector Specifications... 7 SQL Commands... 7 Aggregate Functions... 7 Scalar Functions... 8 Date, Time and Interval Functions... 9 Joins SELECT / Subquery Key Words : SQL Extensions Fuzzy Search in a Where Clause Fuzzy Hamming Search Fuzzy Edit Distance Search Compare Fuzzy Hamming to Fuzzy Edit Distance Exact Search Syntax Fuzzy Search SQL Syntax Search Surrounding Width parameter Case Insensitive Where Clause Supported Wildcards : Supported Metadata Formats Metadata File Format Columns Format JSON Metadata Table VIEW Example : Ryft Tested BI Tools Ryft Systems, Inc. Page iv

5 Using This Guide This guide is for the System Administrator responsible for maintaining the Ryft Cloud Connector on your AWS Ryft instance, and making it accessible to your end users. It s also for the end user who will ultimately use their business intelligence and data visualization (BI/DV) tool to access the datasets, taking advantage of all the benefits of the AWS Ryft-enabled server. Ryft Technical Support You may access our first-tier support directly from our public website by using the Chat Widget and starting a chat with our support agents. All chat conversations are tracked and become help desk tickets in our support system. In addition, you can access our support system through the Ryft Cloud Support site: Log in with your credentials to see your past tickets, create new tickets, and to access limited-access content. For technical support, contact us: support@ryft.com Web: Phone: (RYFT ONE) About Ryft Systems Inc. Ryft makes data analytics fast and simple with the world s top cloud and physical heterogeneous compute accelerators. With more than a decade of experience, Ryft helps deliver instant insights into any data by eliminating the latency associated with data indexing and transformation/curating. Today, industry leaders rely on Ryft to quickly and simply unlock the value hidden in their data for real-time actionable insight. Ryft Systems, Inc. Page v

6 This page intentionally left blank. Ryft Systems, Inc. Page vi

7 1 1: ODBC/JDBC Connector Specifications The Ryft Cloud ODBC/JDBC Connector (Connector) is SQL-92-compliant with comprehensive support for ODBC 3.80-compliant data types, as well as scalar and aggregate functions. Specifically, the Connector supports the following specifications: ANSI and Unicode UTF-8 support for both data and metadata. Available 64-bit versions for Windows, Linux and an MAC. Maximum number of simultaneous connections limited only by memory. Maximum result set length limited only by memory and swap table space. Maximum 65,356 columns supported in a select list. Maximum 65,356 columns supported in a table. Maximum number of characters in a SQL statement is configurable and is only limited by memory. Maximum number of joined tables limited only by memory and swap table space. The following tables summarize the various commands and functions that the Connector supports. SQL Commands Supported CREATE TABLE DROP TABLE INSERT SELECT Not Supported CREATE VIEW DELETE DROP VIEW UPDATE Aggregate Functions Supported AVG COUNT MAX MIN STDDEV STDDEV_POP Not Supported (none) Ryft Systems, Inc. Page 7

8 Supported SUM VAR VAR_POP Not Supported Scalar Functions Supported String Functions ASCII CHAR CONCAT LCASE LEFT LENGTH LOCATE LTRIM INSERT REPEAT REPLACE RTRIM SOUNDEX SPACE SUBSTRING UCASE Numeric Functions ABS ACOS ASIN ATAN ATAN2 CEILING COS COT DEGREES EXP FLOOR LOG LOG10 MOD PI POWER RADIANS Not Supported BIT_LENGTH CHAR LENGTH CHARACTER_LENGTH DIFFERENCE OCTET_LENGTH POSITION(_ IN _) (none) Ryft Systems, Inc. Page 8

9 Supported RAND ROUND SIGN SIN SQRT TAN TRUNCATE System Functions CAST CONVERT IFNULL Not Supported DATABASE USER Date, Time and Interval Functions Supported CURRENT_DATE CURRENT_TIME CURRENT_TIMESTAMP CURDATE CURTIME DAYNAME DAYOFMONTH DAYOFWEEK DAYOFYEAR HOUR INTERVAL INTERVAL DAY INTERVAL HOUR INTERVAL MINUTE INTERVAL MONTH INTERVAL SECOND INTERVAL YEAR MINUTE MONTH MONTHNAME NOW QUARTER SECOND TIMESTAMPDIFF TIMESTAMPADD WEEK YEAR Not Supported CURRENT_TIME(time-precision) CURRENT_TIMESTAMP(time-precision) EXTRACT(_ FROM _) Ryft Systems, Inc. Page 9

10 Joins Supported CROSS JOIN JOIN INNER LEFT OUTER LEFT RIGHT OUTER RIGHT FULL FULL OUTER Not Supported (none) SELECT / Subquery Key Words Supported ALL ANY DISTINCT GROUP BY HAVING LIMIT TOP UNION UNION ALL VALUES AS ASC DATATYPE (one of the SQL_* types) DESC DEFAULT WHERE OR AND NOT EQ (=) NE (<>) LT (<) GT (>) LE (<=) GE (>=) NOT BETWEEN BETWEEN NOT IN Not Supported (none) Ryft Systems, Inc. Page 10

11 Supported IN NOT LIKE LIKE ESCAPE IS NOT NULL IS NULL EXISTS Not Supported * types are limited to numbers, date/ time, and text. Ryft Systems, Inc. Page 11

12 2 2: SQL Extensions The Connector includes extensions that provide the ability to execute certain functions that are not in the SQL-92 standard. These extensions do not require any code modifications for an SQL application. Fuzzy Search in a Where Clause There are two types of fuzzy searches supported by the Connector. These allow the user to create an SQL select statement with additional qualifications on the where clause. The two fuzzy search types supported are: Fuzzy Hamming search Fuzzy Edit Distance (Levenshtein) search Fuzzy Hamming Search The Fuzzy Hamming search operation works similarly to an exact search except that matches do not have to be exact. Instead, the fuzziness parameter allows the specification of a "close enough" value to indicate how close the input must be to match the search criteria. The match string can be up to 32 bytes in length. A "close enough" match is specified as a Hamming distance. The Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different. As provided to the fuzzy search operation, the Hamming distance specifies the maximum number of substitutions that are allowed in order to declare a match. In addition, similar to exact search, the surrounding mechanism can aid in downstream analysis of contextual use of the fuzzy match results against unstructured raw text data Fuzzy Edit Distance Search Fuzzy Edit Distance (Levenshtein) Search performs a search that does not require two strings to be of equal length to obtain a match. Instead of considering individual symbol differences, fuzzy edit distance search counts the minimum number of insertions, deletions and replacements required to transform one string into another. This can make it much more powerful than Fuzzy Hamming search for certain applications. Ryft Systems, Inc. Page 12

13 Compare Fuzzy Hamming to Fuzzy Edit Distance Let s conduct a search for the string Michelle to compare Fuzzy Hamming with Fuzzy Edit Distance, using an edit distance = 1. Search String Fuzzy Hamming Fuzzy Edit Distance, Edit Distance = 1 Michelle Yes. Exact match. Yes. Exact match. Mishelle Yes. c changed to s. Yes. c changed to s. Mischelle Michele Mischele No. The string chelle does not appear in the same position as in the original search term Michelle. This makes it an edit distance of 6, which is greater than the specified distance. No. Deleting one l shortens the string by one character, and the match string must be of equal length. No. Although of equal length, more than one change is required to match. Yes. Can insert the single character c. Yes. One l deleted. No. Requires 2 changes: add an s and remove an l. NOTE: If the edit distance = 2, then it would match since the calculated edit distance between the 2 strings is less than or equal to the desired edit distance (2). Fuzzy edit distance is an extremely powerful search tool for a variety of data sources, including names, addresses, medical records searching, genomic and disease research data, common misspellings, and more. Unlike Fuzzy Hamming search, Fuzzy Edit Distance is a more natural fuzzy search paradigm for many algorithms, since it does not require string matches to be of the same size. Exact Search Syntax The syntax for exact search is as follows: select <xxx> from <table> where <field> like '<term_to_match>' It s a basic select statement. Fuzzy Search SQL Syntax For fuzzy search, the syntax is modified in the where clause match statement as follow: select <xxx> from <table> where <column> like '-<fuzzy_type><distance>(<term_to_match>)' <fuzzy_type> = h H denoting hamming search or e E denoting edit distance search. <distance> = integer, max = 255. The fuzziness of the search up to a maximum of 255 when using a fuzzy search function. For fuzzy hamming search, fuzziness is measured as the maximum Ryft Systems, Inc. Page 13

14 Hamming distance allowed in order to declare a match. For fuzzy edit distance search, fuzziness is measured as the number of insertions, deletions or replacements required to declare a match. Example: This command executes a fuzzy edit distance search (distance=2) select * from Passengers where Name like '-E2(Michelle)' Search Surrounding Width parameter The surrounding Width parameter enables you to specify the number of characters, in bytes up to a maximum of 262,144, before and after the match that will be returned when using text search. The syntax is modified in the where clause match statement as follow: select where <column> like '-<fuzzy_type><distance>(<term_to_match>)-w<width>' <width> = integer denotes the number of bytes (characters) before and after the match. Example: This command executes a fuzzy edit distance search (distance=2) with a surrounding width of 4. select * from Passengers where Name like '-E2(Michelle)-W4' The result includes 4 bytes/characters before and after Michelle. NOTE: Width is only used for raw text queries, otherwise it looks at the entire record. Case Insensitive Where Clause The Connector provides the ability to execute a query that is case-insensitive or case-sensitive. By default, all queries are case-sensitive. The case-insensitive selection is made by using the -i parameter in the where clause. Example: The following matches the name Michelle in any combination of upper or lower case. select * from Passengers where Name like '(Michelle)-i' Supported Wildcards Wildcards are supported by using the standard % symbol. For example: select * from <table_name> where Description LIKE '%ELECTRONIC%' Ryft Systems, Inc. Page 14

15 3 3: Supported Metadata Formats The ODBC/JDBC connector requires definition of the metadata. Metadata is used to provide the SQL engine the appropriate data type associated with each data column returned. Its defined in the.meta.table file, which exists for each dataset on the Ryft instance. The metadata file contains information about the associated data file, the data fields (columns) to return from the dataset and the data type for each field, and the opportunity to pre-define a search to generate an SQL view. This is a simple data file called Passengers.pxml that contains 4 fields for each record: Name, DoB, Phone, and Notes: $ cat Passengers.pxml <?xml version = "1.0" encoding = "UTF-8"?> <xml_root> <csv_record> <Name>Hannibal Smith</Name> <DoB> </DoB> <Phone> </Phone> <Notes>A-team, baby, A-team!</Notes> </csv_record> <csv_record> <Name>DR. Thomas Magnum</Name> <DoB> </DoB> <Phone> </Phone> <Notes>Magnum PI himself.</notes> </csv_record> It s related.meta.table file contains this information about the data and includes a simple view at the end of the file: # table metadata definition file table_name = "Passengers" table_remarks = "Action hero passenger database" version = 2 file_glob = "*.pxml" data_type = "XML" record_tag = "<csv_record>" columns = { Name = ( "Name","VARCHAR(80)", "" ), DoB = ( "Date_of_Birth", "VARCHAR(80)", "" ), Phone = ( "Phone","VARCHAR(80)", "" ), Notes = ( "Notes","VARCHAR(256)", "" ), }; Ryft Systems, Inc. Page 15

16 views = { Passengers_Named_T = ( "Passengers_Named_T", "(RECORD.Name EQUALS \"T\")", "Action hero passengers with the name T" ), }; Metadata File Format The.meta.data file contains the following information: Field table_name table_remarks version data_type file_glob record_tag record_path columns views Description/Notes Name of the data table recognized by the SQL engine. Information about or description of the data table. Table release version. Equal to 2 for AWS Data format. Options are XML, JSON, or RAW The file extension for all files which are included in table (directory structure) for searching For XML only. The tag name for the start / end of each record. For JSON only. Denotes use of a top level array or not. Valid values are [] or * List of fields/columns associated with the dataset, including data type and any optional description. Optional. Pre-defined search (view) of the data that generates a SQL view. Columns Format The columns format will vary depending on the data type. In general, most data types use this format: <tag_name> = ( <column_name>, <data_type>, <optional_description> ) The <tag_name> can only contain alphanumeric characters. However, an escape character may be used for cases where <tag_name> contains spaces or special characters. To use the escaped character, use the format of *nn where nn is the hexadecimal value of the character you are embedding in the tag_name. For example: Tag*20Name creates a tag called Tag Name. Each supported data type is included, below, with the <data_type> identified for each: Data Type INTEGER BIGINT VARCHAR Format and Information INTEGER Integer data. BIGINT Integer data larger than 2,147,483,647. VARCHAR(x) Ryft Systems, Inc. Page 16

17 Data Type DOUBLE NUMBER CURRENCY DATETIME Format and Information x = integer denoting the number of alphanumeric characters, such as VARCHAR(16) DOUBLE Double precision floating point. NUMBER(x,<separator>,<decimal>) x = integer denoting the number of characters. separator = the separating character to use. For example, for standard US numbers, a comma would be specified. If other types of numbers are being searched, such as perhaps phone numbers, then a dash would be specified decimal = the decimal specifier to use. For example, for standard US numbers, a period would be specified. For example: NUMBER(16,,,.) CURRENCY(x,<currency>,<separator>,<decimal>) x = integer denoting the number of characters. currency = currency identifier, such as "$" for US currency. separator = the optional separating character that may be encountered when parsing currency. For example, for standard US currency, a comma would be specified. decimal = the decimal specifier to use. For example, for standard US numbers, a period would be specified. For example: CURRENCY(16,$,,,.) DATETIME(<date-time_format>) date-time_format = the format for date and time in the data set. The connector automatically converts the date/time formats in the dataset to the standard SQL formats. The supported formats for each are listed below. Date formats: YYYY/MM/DD YY/MM/DD DD/MM/YYYY DD/MM/YY MM/DD/YYYY MM/DD/YY The "/" character in the above list of formats can be replaced by any other single character delimiter. For example, YYYY-MM-DD and MM_DD_YYYY are both acceptable date formats. Time formats: HH:MM:SS NOTE: If the dataset contains time in 12-hour (AM /PM) format, then replace the HH with 12, i.e.: 12:MM:SS. Otherwise, it s assumed time is in 24-hour format. Ryft Systems, Inc. Page 17

18 Data Type DATE TIME Format and Information Example: <tag_name> = ( <column_name>, DATETIME(MM/DD/YYYY HH:MM:SS), <optional_description> ) DATE(<date format>) date_format = the format for date in the data set. The connector automatically converts the date formats in the dataset to the standard SQL formats. The supported format for date are listed below. Date formats: YYYY/MM/DD YY/MM/DD DD/MM/YYYY DD/MM/YY MM/DD/YYYY MM/DD/YY The "/" character in the above list of formats can be replaced by any other single character delimiter. For example, YYYY-MM-DD and MM_DD_YYYY are both acceptable date formats. Example: <tag_name> = ( <column_name>, DATE(MM/DD/YYYY), <optional_description> ) SQL Sample Query: select * from NYC_Taxi where dropoff_datetime >= TIMESTAMP ' :00:00' and dropoff_datetime <= TIMESTAMP ' :59:59' TIME <time format> time_format = the format for time in the data set. The connector automatically converts the time formats in the dataset to the standard SQL formats. The supported format for time is HH:MM:SS NOTE: If the dataset contains time in 12 hour (AM /PM) format, then the HH should be replaced with 12, i.e.: 12:MM:SS Example: <tag_name> = ( <column_name>, DATETIME(MM/DD/YYYY HH:MM:SS), <optional_description> ) SQL Sample Query #1: select Ward, District, Primary_Type, Description from Chicago_Crime where Date = TIME '11:53:00' SQL Sample Query #2: select Ward, District, Primary_Type, Description from Chicago_Crime where Date = DATE ' ' and Date >= TIME '08:30:00' and Date <= TIME '09:30:00' METAVAR METAVAR(<value>) Ryft Systems, Inc. Page 18

19 Data Type Format and Information Value = FILE OFFSET LENGTH The metavar is as special case used for obtaining column data associated with an index file. Typically, this is used when the connector is performing the query on nonstructured datasets. The metavar type will only have 3 possible values, as noted above. For example, this is the.meta.table for Wikipedia, a non-structured file. # table metadata definition file table_name = "Wikipedia" table_remarks = "This table is associated with the wikipedia raw text file" rdf = "/ryftone/odbc/wikipedia/wikipedia.rdf"; columns = { Results = ( "Results","VARCHAR(65535)", "" ), FILE = ( "WIKI_FILE","METAVAR(FILE)", ""), OFFSET = ( "WIKI_OFFSET", "METAVAR(OFFSET)", ""), LENGTH = ( "WIKI_LENGTH", "METAVAR(LENGTH)", ""), }; JSON Metadata The.meta.table enables you to define nested lists, arrays and groups within the JSON dataset. Data Type ARRAYOF GROUPOF LIST Format and Information ARRAYOF(<name>) name = name of metadata sub-column definition that is contained later in the metadata file. NOTE: Elements contained in the array can only be of type VARCHAR and the size of the VARCHAR string must be large enough to contain the entire array of this element of the group plus space for n-1 delimiters. GROUPOF(<name>) name = name of metadata sub-column definition that is contained later in the metadata file. A group can contain columns of any basic type or nest GROUPOF, ARRAYOF or LIST types. LIST(<x>) x = integer denoting the number of alphanumeric characters required to store the entire list plus space for n-1 delimiters, such as LIST(4096) LIST is like ARRAYOF except that elements are unnamed, e.g. [ Red, Green, Blue ]. This sample file contains examples of each, as denoted by the GROUPOF and ARRAYOF data types. Ryft Systems, Inc. Page 19

20 # table metadata definition file table_name = "Seuss_Books" table_remarks = "Dr. Seuss Books" version = 2 data_type = "JSON" file_glob = "*.seuss" record_path = "[]" columns = { title = ( "Title","VARCHAR(128)", "" ), format = ( "Format", "GROUPOF(formats)", "" ), category = ( "Category", "ARRAYOF(category)", "" ), }; category = { class = ("Classification", "VARCHAR(4096)", ""), theme = ("Theme", "VARCHAR(4096)", ""), }; formats = { Softcopy = ("Paperback", "VARCHAR(1)", ""), Hardcover = ("Hardcover", "VARCHAR(1)", ""), }; This is the associated JSON dataset: :::::::::::::: books2.seuss :::::::::::::: [ { "title": "Dr. Suess's ABC", "format": {"Softcopy" : "Y", "Hardcover" : "N"}, "category": [ { "class": "Juvenile Fiction", "theme": "Concepts - Alphabet" }, { "class": "Adult Fiction", "theme": "Readers - Beginner" }, { "class": "Young Adult Fiction", "theme": "Classics" }]}, { "title": "Fox in Socks", "format": {"Softcopy" : "N", "Hardcover" : "Y"}, "category": [ { "class": "Juvenile Fiction", "theme": "Animals - Foxes" }, { "class": "Juvenile Fiction", "theme": "Concepts - Sounds" }, { "class": "Juvenile Fiction", "theme": "Readers - Beginner" }]}, { "title": "There's a Wocket in My Pocket", "format": {"Softcopy" : "Y", "Hardcover" : "N"}, "category": [ { "class": "Juvenile Fiction", "theme": "Concepts - Alphabet" }]} ] :::::::::::::: books.seuss :::::::::::::: [ { "title": "Dr. Suess's ABC", "format": {"Softcopy" : "Y", "Hardcover" : "Y"}, "category": [ { "class": "Juvenile Fiction", "theme": "Concepts - Alphabet" }, { "class": "Juvenile Fiction", "theme": "Readers - Beginner" }, { "class": "Juvenile Fiction", "theme": "Classics" }]}, { "title": "Fox in Socks", "format": {"Softcopy" : "Y", "Hardcover" : "Y"}", "category": [ { "class": "Juvenile Fiction", "theme": "Animals - Foxes" }, { "class": "Juvenile Fiction", "theme": "Concepts - Sounds" }, { "class": "Juvenile Fiction", "theme": "Readers - Beginner" }]}, { "title": "There's a Wocket in My Pocket", "format": {"Softcopy" : "Y", "Hardcover" : "Y"}, "category": [ { "class": "Juvenile Fiction", "theme": "Concepts - Words" }]} ] Ryft Systems, Inc. Page 20

21 Here is a typical SQL Query using the GROUPOF or ARRAYOF data types. Doc #1208, V1.0.0/ October 2017 Table VIEW Example A pre-defined VIEW can be configured in the.meta.table file. This will automatically execute the defined query whenever the VIEW is selected as a table to view by the SQL client. For example, two views are defined, below, to execute two queries. The views created are the Chicago_Crimes_with_Arrest and Chicago_Crimes_with_no_Arrest # table metadata definition file table_name = "Chicago_Crime" table_remarks = "Chicago Crime Statistics" version = 2 data_type = "XML" file_glob = "*.pcrime" record_tag = "<rec>" columns = { ID = ( "ID", "INTEGER", ""), CaseNumber = ( "Case_Number", "VARCHAR(80)","" ), Date = ( "Date", "DATETIME(MM/DD/YYYY 12:MM:SS)", "" ), Block = ( "Block", "VARCHAR(256)", "" ), IUCR = ( "IUCR", "VARCHAR(256)", "" ), PrimaryType = ( "Primary_Type", "VARCHAR(80)",">" ), Description = ( "Description", "VARCHAR(1024)","" ), LocationDescription = ( "LocDesc", "VARCHAR(1024)", "" ), Arrest = ( "Arrest", "VARCHAR(80","" ), Domestic = ( "Domestic", "VARCHAR(80)", "" ), Beat = ( "Beat", "NUMBER(16,,,.)", "" ), District = ( "District", "VARCHAR(10)", "" ), Ward = ( "Ward", "NUMBER(16,,,.)", "" ), CommunityArea = ( "Community_Area", "NUMBER(16,,,.)", "" ), FBICode = ( "FBI_Code", "VARCHAR(256)", "" ), XCoordinate = ( "XCoordinate", "NUMBER(32,,,.)", "" ), YCoordinate = ( "YCoordinate", "NUMBER(32,,,.)", "" ), Year = ( "Year", "NUMBER(16,,,.)", "" ), UpdatedOn = ( "Updated_On", "VARCHAR(80)", "" ), Latitude = ( "Latitude", "DOUBLE", "" ), Longitude = ( "Longitude", "DOUBLE", "" ), Location = ( "Location", "VARCHAR(1024)", "" ) }; views = { Chicago_Crimes_Arrest = ( "Chicago_Crimes_with_Arrest", "(RECORD.Arrest EQUALS \"true\")", "Chicago crimes that resulted in an arrest"), Chicago_Crimes_No_Arrest = ( "Chicago_Crimes_with_no_Arrest", "(RECORD.Arrest NOT_EQUALS \"true\")", Ryft Systems, Inc. Page 21

22 }; "Chicago crimes that did not result in an arrest"), Ryft Systems, Inc. Page 22

23 4 4: Ryft Tested BI Tools The ODBC / JDBC connector will operate with any SQL-92 standard-compliant Business Intelligence (BI) tool. Ryft has verified and tested against select number of these BI tools to ensure we can connect and operate with the tool. The table below provides a list of the SQL-92 standard-compliant BI tools, and notes those that are Ryft verified. Ryft Verified Vendor Supported VENDOR ODBC JDBC ODBC JDBC Birst Conduce ~ ~ IBM Information Builders Jaspersoft LibreOffice Logi Analytics Microsoft Microstrategy Oracle Pentaho Qlik 1 SAP SAS Tableau Tibco Software Zoomdata 1: Qlick JDBC requires 3rd party tool Ryft Systems, Inc. Page 23