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  • Android: Music track visualization

    - by Swathi EP
    Hello all, I want to create an music track visualization for an music player application which should look like as below: you can see in the above image that, there is an equalizer kind of view and it should vary as the music track is played. I need to know the right way to achieve the above visualization, which api to use?, etc., Any suggestion will be greatly helpful to me. Thanks, Swathi

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  • Shrinking TCP Window Size to 0 on Cisco ASA

    - by Brent
    Having an issue with any large file transfer that crosses our Cisco ASA unit come to an eventual pause. Setup Test1: Server A, FileZilla Client <- 1GBPS - Cisco ASA <- 1 GBPS - Server B, FileZilla Server TCP Window size on large transfers will drop to 0 after around 30 seconds of a large file transfer. RDP session then becomes unresponsive for a minute or two and then is sporadic. After a minute or two, the FTP transfer resumes, but at 1-2 MB/s. When the FTP transfer is over, the responsiveness of the RDP session returns to normal. Test2: Server C in same network as Server B, FileZilla Client <- local network - Server B, FileZilla Server File will transfer at 30+ MB/s. Details ASA: 5520 running 8.3(1) with ASDM 6.3(1) Windows: Server 2003 R2 SP2 with latest patches Server: VMs running on HP C3000 blade chasis FileZilla: 3.3.5.1, latest stable build Transfer: 20 GB SQL .BAK file Protocol: Active FTP over tcp/20, tcp/21 Switches: Cisco Small Business 2048 Gigabit running latest 2.0.0.8 VMware: 4.1 HP: Flex-10 3.15, latest version Notes All servers are VMs. Thoughts Pretty sure the ASA is at fault since a transfer between VMs on the same network will not show a shrinking Window size. Our ASA is pretty vanilla. No major changes made to any of the settings. It has a bunch of NAT and ACLs. Wireshark Sample No. Time Source Destination Protocol Info 234905 73.916986 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131981791 Win=65535 Len=0 234906 73.917220 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234907 73.917224 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234908 73.917231 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131984551 Win=64155 Len=0 234909 73.917463 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234910 73.917467 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234911 73.917469 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234912 73.917476 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131988691 Win=60015 Len=0 234913 73.917706 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234914 73.917710 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234915 73.917715 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131991451 Win=57255 Len=0 234916 73.917949 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234917 73.917953 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234918 73.917958 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131994211 Win=54495 Len=0 234919 73.918193 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234920 73.918197 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234921 73.918202 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131996971 Win=51735 Len=0 234922 73.918435 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234923 73.918440 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234924 73.918445 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131999731 Win=48975 Len=0 234925 73.918679 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234926 73.918684 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234927 73.918689 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132002491 Win=46215 Len=0 234928 73.918922 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234929 73.918927 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234930 73.918932 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132005251 Win=43455 Len=0 234931 73.919165 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234932 73.919169 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234933 73.919174 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132008011 Win=40695 Len=0 234934 73.919408 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234935 73.919413 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234936 73.919418 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132010771 Win=37935 Len=0 234937 73.919652 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234938 73.919656 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234939 73.919661 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132013531 Win=35175 Len=0 234940 73.919895 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234941 73.919899 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234942 73.919904 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132016291 Win=32415 Len=0 234943 73.920138 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234944 73.920142 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234945 73.920147 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132019051 Win=29655 Len=0 234946 73.920381 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234947 73.920386 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234948 73.920391 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132021811 Win=26895 Len=0 234949 73.920625 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234950 73.920629 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234951 73.920632 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234952 73.920638 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132025951 Win=22755 Len=0 234953 73.920868 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234954 73.920871 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234955 73.920876 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132028711 Win=19995 Len=0 234956 73.921111 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234957 73.921115 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234958 73.921120 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132031471 Win=17235 Len=0 234959 73.921356 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234960 73.921362 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234961 73.921370 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132034231 Win=14475 Len=0 234962 73.921598 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234963 73.921606 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234964 73.921613 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132036991 Win=11715 Len=0 234965 73.921841 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234966 73.921848 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234967 73.921855 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132039751 Win=8955 Len=0 234968 73.922085 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234969 73.922092 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234970 73.922099 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132042511 Win=6195 Len=0 234971 73.922328 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234972 73.922335 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234973 73.922342 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132045271 Win=3435 Len=0 234974 73.922571 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234975 73.922579 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234976 73.922586 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132048031 Win=675 Len=0 234981 75.866453 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 675 bytes 234985 76.020168 1.1.1.1 2.2.2.2 TCP [TCP ZeroWindow] ftp-data ivecon-port [ACK] Seq=1 Ack=132048706 Win=0 Len=0 234989 76.771633 2.2.2.2 1.1.1.1 TCP [TCP ZeroWindowProbe] ivecon-port ftp-data [ACK] Seq=132048706 Ack=1 Win=65535 Len=1 234990 76.771648 1.1.1.1 2.2.2.2 TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] ftp-data ivecon-port [ACK] Seq=1 Ack=132048706 Win=0 Len=0 234997 78.279701 2.2.2.2 1.1.1.1 TCP [TCP ZeroWindowProbe] ivecon-port ftp-data [ACK] Seq=132048706 Ack=1 Win=65535 Len=1 234998 78.279714 1.1.1.1 2.2.2.2 TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] ftp-data ivecon-port [ACK] Seq=1 Ack=132048706 Win=0 Len=0

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  • Shrinking Windows Size to 0 on Cisco ASA

    - by Brent
    Having an issue with any large file transfer that crosses our Cisco ASA unit come to an eventual pause. Setup Test1: Server A, FileZilla Client <- 1GBPS - Cisco ASA <- 1 GBPS - Server B, FileZilla Server TCP Window size on large transfers will drop to 0 after around 30 seconds of a large file transfer. RDP session then becomes unresponsive for a minute or two and then is sporadic. After a minute or two, the FTP transfer resumes, but at 1-2 MB/s. When the FTP transfer is over, the responsiveness of the RDP session returns to normal. Test2: Server C in same network as Server B, FileZilla Client <- local network - Server B, FileZilla Server File will transfer at 30+ MB/s. Details ASA: 5520 running 8.3(1) with ASDM 6.3(1) Windows: Server 2003 R2 SP2 with latest patches Server: VMs running on HP C3000 blade chasis FileZilla: 3.3.5.1, latest stable build Transfer: 20 GB SQL .BAK file Protocol: Active FTP over tcp/20, tcp/21 Switches: Cisco Small Business 2048 Gigabit running latest 2.0.0.8 VMware: 4.1 HP: Flex-10 3.15, latest version Notes All servers are VMs. Thoughts Pretty sure the ASA is at fault since a transfer between VMs on the same network will not show a shrinking Window size. Our ASA is pretty vanilla. No major changes made to any of the settings. It has a bunch of NAT and ACLs. Wireshark Sample No. Time Source Destination Protocol Info 234905 73.916986 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131981791 Win=65535 Len=0 234906 73.917220 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234907 73.917224 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234908 73.917231 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131984551 Win=64155 Len=0 234909 73.917463 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234910 73.917467 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234911 73.917469 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234912 73.917476 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131988691 Win=60015 Len=0 234913 73.917706 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234914 73.917710 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234915 73.917715 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131991451 Win=57255 Len=0 234916 73.917949 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234917 73.917953 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234918 73.917958 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131994211 Win=54495 Len=0 234919 73.918193 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234920 73.918197 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234921 73.918202 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131996971 Win=51735 Len=0 234922 73.918435 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234923 73.918440 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234924 73.918445 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=131999731 Win=48975 Len=0 234925 73.918679 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234926 73.918684 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234927 73.918689 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132002491 Win=46215 Len=0 234928 73.918922 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234929 73.918927 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234930 73.918932 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132005251 Win=43455 Len=0 234931 73.919165 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234932 73.919169 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234933 73.919174 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132008011 Win=40695 Len=0 234934 73.919408 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234935 73.919413 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234936 73.919418 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132010771 Win=37935 Len=0 234937 73.919652 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234938 73.919656 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234939 73.919661 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132013531 Win=35175 Len=0 234940 73.919895 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234941 73.919899 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234942 73.919904 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132016291 Win=32415 Len=0 234943 73.920138 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234944 73.920142 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234945 73.920147 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132019051 Win=29655 Len=0 234946 73.920381 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234947 73.920386 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234948 73.920391 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132021811 Win=26895 Len=0 234949 73.920625 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234950 73.920629 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234951 73.920632 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234952 73.920638 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132025951 Win=22755 Len=0 234953 73.920868 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234954 73.920871 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234955 73.920876 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132028711 Win=19995 Len=0 234956 73.921111 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234957 73.921115 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234958 73.921120 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132031471 Win=17235 Len=0 234959 73.921356 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234960 73.921362 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234961 73.921370 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132034231 Win=14475 Len=0 234962 73.921598 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234963 73.921606 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234964 73.921613 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132036991 Win=11715 Len=0 234965 73.921841 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234966 73.921848 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234967 73.921855 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132039751 Win=8955 Len=0 234968 73.922085 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234969 73.922092 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234970 73.922099 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132042511 Win=6195 Len=0 234971 73.922328 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234972 73.922335 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234973 73.922342 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132045271 Win=3435 Len=0 234974 73.922571 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234975 73.922579 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 1380 bytes 234976 73.922586 1.1.1.1 2.2.2.2 TCP ftp-data ivecon-port [ACK] Seq=1 Ack=132048031 Win=675 Len=0 234981 75.866453 2.2.2.2 1.1.1.1 FTP-DATA FTP Data: 675 bytes 234985 76.020168 1.1.1.1 2.2.2.2 TCP [TCP ZeroWindow] ftp-data ivecon-port [ACK] Seq=1 Ack=132048706 Win=0 Len=0 234989 76.771633 2.2.2.2 1.1.1.1 TCP [TCP ZeroWindowProbe] ivecon-port ftp-data [ACK] Seq=132048706 Ack=1 Win=65535 Len=1 234990 76.771648 1.1.1.1 2.2.2.2 TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] ftp-data ivecon-port [ACK] Seq=1 Ack=132048706 Win=0 Len=0 234997 78.279701 2.2.2.2 1.1.1.1 TCP [TCP ZeroWindowProbe] ivecon-port ftp-data [ACK] Seq=132048706 Ack=1 Win=65535 Len=1 234998 78.279714 1.1.1.1 2.2.2.2 TCP [TCP ZeroWindowProbeAck] [TCP ZeroWindow] ftp-data ivecon-port [ACK] Seq=1 Ack=132048706 Win=0 Len=0

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  • Using Google Visualization in GWT 2.0

    - by nick
    I'm working on learning GWT (total newb) and have a question regarding the Visualiztion API provided by Google. This page: http://code.google.com/p/gwt-google-apis/wiki/VisualizationGettingStarted Describes getting started with a pie chart (which is what I need). However I'm trying to do this in a composite UI using UiBinder. To that end I don't know how to handle the callback correctly that is shown: public class SimpleViz implements EntryPoint { public void onModuleLoad() { // Create a callback to be called when the visualization API // has been loaded. Runnable onLoadCallback = new Runnable() { public void run() { Panel panel = RootPanel.get(); // Create a pie chart visualization. PieChart pie = new PieChart(createTable(), createOptions()); pie.addSelectHandler(createSelectHandler(pie)); panel.add(pie); } }; // Load the visualization api, passing the onLoadCallback to be called // when loading is done. VisualizationUtils.loadVisualizationApi(onLoadCallback, PieChart.PACKAGE); } My First assumption is this would go in the UiBinder constructor, correct? Yet this assumes that I want to place the element in the RootLayoutPanel, and I don't. I can't see an elegant and obvious way of placing it in the binder. I submit that even this guess may be wrong. Any ideas from the experts?

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  • Big Data Appliance X4-2 Release Announcement

    - by Jean-Pierre Dijcks
    Today we are announcing the release of the 3rd generation Big Data Appliance. Read the Press Release here. Software Focus The focus for this 3rd generation of Big Data Appliance is: Comprehensive and Open - Big Data Appliance now includes all Cloudera Software, including Back-up and Disaster Recovery (BDR), Search, Impala, Navigator as well as the previously included components (like CDH, HBase and Cloudera Manager) and Oracle NoSQL Database (CE or EE). Lower TCO then DIY Hadoop Systems Simplified Operations while providing an open platform for the organization Comprehensive security including the new Audit Vault and Database Firewall software, Apache Sentry and Kerberos configured out-of-the-box Hardware Update A good place to start is to quickly review the hardware differences (no price changes!). On a per node basis the following is a comparison between old and new (X3-2) hardware: Big Data Appliance X3-2 Big Data Appliance X4-2 CPU 2 x 8-Core Intel® Xeon® E5-2660 (2.2 GHz) 2 x 8-Core Intel® Xeon® E5-2650 V2 (2.6 GHz) Memory 64GB 64GB Disk 12 x 3TB High Capacity SAS 12 x 4TB High Capacity SAS InfiniBand 40Gb/sec 40Gb/sec Ethernet 10Gb/sec 10Gb/sec For all the details on the environmentals and other useful information, review the data sheet for Big Data Appliance X4-2. The larger disks give BDA X4-2 33% more capacity over the previous generation while adding faster CPUs. Memory for BDA is expandable to 512 GB per node and can be done on a per-node basis, for example for NameNodes or for HBase region servers, or for NoSQL Database nodes. Software Details More details in terms of software and the current versions (note BDA follows a three monthly update cycle for Cloudera and other software): Big Data Appliance 2.2 Software Stack Big Data Appliance 2.3 Software Stack Linux Oracle Linux 5.8 with UEK 1 Oracle Linux 6.4 with UEK 2 JDK JDK 6 JDK 7 Cloudera CDH CDH 4.3 CDH 4.4 Cloudera Manager CM 4.6 CM 4.7 And like we said at the beginning it is important to understand that all other Cloudera components are now included in the price of Oracle Big Data Appliance. They are fully supported by Oracle and available for all BDA customers. For more information: Big Data Appliance Data Sheet Big Data Connectors Data Sheet Oracle NoSQL Database Data Sheet (CE | EE) Oracle Advanced Analytics Data Sheet

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  • Review: Data Modeling 101

    I just recently read “Data Modeling 101”by Scott W. Ambler where he gave an overview of fundamental data modeling skills. I think this article was excellent for anyone who was just starting to learn or refresh their skills in regards to the modeling of data.  Scott defines data modeling as the act of exploring data oriented structures.  He goes on to explain about how data models are actually used by defining three different types of models. Types of Data Models Conceptual Data Model  Logical Data Model (LDMs) Physical Data Model(PDMs) He further expands on modeling by exploring common data modeling notations because there are no industry standards for the practice of data modeling. Scott then defines how to actually model data by expanding on entities, attributes, identities, and relationships which are the basic building blocks of data models. In addition he discusses the value of normalization for redundancy and demoralization for performance. Finally, he discuss ways in which Developers and DBAs can become better data modelers through the use of practice, and seeking guidance from more experienced data modelers.

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  • HTML5 data-* (custom data attribute)

    - by Renso
    Goal: Store custom data with the data attribute on any DOM element and retrieve it. Previously under HTML4 we used to use classes to store custom data, something to the affect of <input class="account void limit-5000 over-4999" /> and then have to parse the data out of the class In a book published by Peter-Paul Koch in 2007, ppk on JavaScript, he explains why and how to use custom attributes to make data more accessible to JavaScript, using name-value pairs. Accessing a custom attribute account-limit=5000 is much easier and more intuitive than trying to parse it out of a class, Plus, what if the class name for example "color-5" has a representative class definition in a CSS stylesheet that hides it away or worse some JavaScript plugin that automatically adds 5000 to it, or something crazy like that, just because it is a valid class name. As you can see there are quite a few reasons why using classes is a bad design and why it was important to define custom data attributes in HTML5. Syntax: You define the data attribute by simply prefixing any data item you want to store with any HTML element with "data-". For example to store our customers account data with a hidden input element: <input type="hidden" data-account="void" data-limit=5000 data-over=4999  /> How to access the data: account  -     element.dataset.account limit    -     element.dataset.limit You can also access it by using the more traditional get/setAttribute method or if using jQuery $('#element').attr('data-account','void') Browser support: All except for IE. There is an IE hack around this at http://gist.github.com/362081. Special Note: Be AWARE, do not use upper-case when defining your data elements as it is all converted to lower-case when reading it, so: data-myAccount="A1234" will not be found when you read it with: element.dataset.myAccount Use only lowercase when reading so this will work: element.dataset.myaccount

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  • Master Data Management – A Foundation for Big Data Analysis

    - by Manouj Tahiliani
    While Master Data Management has crossed the proverbial chasm and is on its way to becoming mainstream, businesses are being hammered by a new megatrend called Big Data. Big Data is characterized by massive volumes, its high frequency, the variety of less structured data sources such as email, sensors, smart meters, social networks, and Weblogs, and the need to analyze vast amounts of data to determine value to improve upon management decisions. Businesses that have embraced MDM to get a single, enriched and unified view of Master data by resolving semantic discrepancies and augmenting the explicit master data information from within the enterprise with implicit data from outside the enterprise like social profiles will have a leg up in embracing Big Data solutions. This is especially true for large and medium-sized businesses in industries like Retail, Communications, Financial Services, etc that would find it very challenging to get comprehensive analytical coverage and derive long-term success without resolving the limitations of the heterogeneous topology that leads to disparate, fragmented and incomplete master data. For analytical success from Big Data or in other words ROI from Big Data Investments, businesses need to acquire, organize and analyze the deluge of data to make better decisions. There will need to be a coexistence of structured and unstructured data and to maintain a tight link between the two to extract maximum insights. MDM is the catalyst that helps maintain that tight linkage by providing an understanding about the identity, characteristics of Persons, Companies, Products, Suppliers, etc. associated with the Big Data and thereby help accelerate ROI. In my next post I will discuss about patterns for co-existing Big Data Solutions and MDM. Feel free to provide comments and thoughts on above as well as Integration or Architectural patterns.

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  • How to Assure an Effective Data Model

    As a general rule in my opinion the effectiveness of a data model can be directly related to the accuracy and complexity of a project’s requirements. For example there is no need to work on very detailed data models when the details surrounding a specific data model have not been defined or even clarified. Developing data models when the clarity of project requirements is limited tends to introduce designed issues because the proper details to create an effective data model are not even known. One way to avoid this issue is to create data models that correspond to the complexity of the existing project requirements so that when requirements are updated then new data models can be created based any new discoveries regarding requirements on a fine grain level.  This allows for data models to be composed of general entities to be created initially when a project’s requirements are very vague and then the entities are refined as new and more substantial requirements are defined or redefined. This promotes communication amongst all stakeholders within a project as they go through the process of defining and finalizing project requirements.In addition, here are some general tips that can be applied to projects in regards to data modeling.Initially model all data generally and slowly reactor the data model as new requirements and business constraints are applied to a project.Ensure that data modelers have the proper tools and training they need to design a data model accurately.Create a common location for all project documents so that everyone will be able to review a project’s data models along with any other project documentation.All data models should follow a clear naming schema that tells readers the intended purpose for the data and how it is going to be applied within a project.

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  • SQL SERVER – Why Do We Need Data Quality Services – Importance and Significance of Data Quality Services (DQS)

    - by pinaldave
    Databases are awesome.  I’m sure my readers know my opinion about this – I have made SQL Server my life’s work after all!  I love technology and all things computer-related.  Of course, even with my love for technology, I have to admit that it has its limits.  For example, it takes a human brain to notice that data has been input incorrectly.  Computer “brains” might be faster than humans, but human brains are still better at pattern recognition.  For example, a human brain will notice that “300” is a ridiculous age for a human to be, but to a computer it is just a number.  A human will also notice similarities between “P. Dave” and “Pinal Dave,” but this would stump most computers. In a database, these sorts of anomalies are incredibly important.  Databases are often used by multiple people who rely on this data to be true and accurate, so data quality is key.  That is why the improved SQL Server features Master Data Management talks about Data Quality Services.  This service has the ability to recognize and flag anomalies like out of range numbers and similarities between data.  This allows a human brain with its pattern recognition abilities to double-check and ensure that P. Dave is the same as Pinal Dave. A nice feature of Data Quality Services is that once you set the rules for the program to follow, it will not only keep your data organized in the future, but go to the past and “fix up” any data that has already been entered.  It also allows you do combine data from multiple places and it will apply these rules across the board, so that you don’t have any weird issues that crop up when trying to fit a round peg into a square hole. There are two parts of Data Quality Services that help you accomplish all these neat things.  The first part is DQL Server, which you can think of as the hardware component of the system.  It is installed on the side of (it needs to install separately after SQL Server is installed) SQL Server and runs quietly in the background, performing all its cleanup services. DQS Client is the user interface that you can interact with to set the rules and check over your data.  There are three main aspects of Client: knowledge base management, data quality projects and administration.  Knowledge base management is the part of the system that allows you to set the rules, or program the “knowledge base,” so that your database is clean and consistent. Data Quality projects are what run in the background and clean up the data that is already present.  The administration allows you to check out what DQS Client is doing, change rules, and generally oversee the entire process.  The whole process is user-friendly and a pleasure to use.  I highly recommend implementing Data Quality Services in your database. Here are few of my blog posts which are related to Data Quality Services and I encourage you to try this out. SQL SERVER – Installing Data Quality Services (DQS) on SQL Server 2012 SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS SQL SERVER – DQS Error – Cannot connect to server – A .NET Framework error occurred during execution of user-defined routine or aggregate “SetDataQualitySessions” – SetDataQualitySessionPhaseTwo SQL SERVER – Configuring Interactive Cleansing Suggestion Min Score for Suggestions in Data Quality Services (DQS) – Sensitivity of Suggestion SQL SERVER – Unable to DELETE Project in Data Quality Projects (DQS) Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • Welcome Oracle Data Integration 12c: Simplified, Future-Ready Solutions with Extreme Performance

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 The big day for the Oracle Data Integration team has finally arrived! It is my honor to introduce you to Oracle Data Integration 12c. Today we announced the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. With the 12c release Oracle becomes the new leader in the data integration and replication technologies as no other vendor offers such a complete set of data integration capabilities for pervasive, continuous access to trusted data across Oracle platforms as well as third-party systems and applications. Oracle Data Integration 12c release addresses data-driven organizations’ critical and evolving data integration requirements under 3 key themes: Future-Ready Solutions Extreme Performance Fast Time-to-Value       There are many new features that support these key differentiators for Oracle Data Integrator 12c and for Oracle GoldenGate 12c. In this first 12c blog post, I will highlight only a few:·Future-Ready Solutions to Support Current and Emerging Initiatives: Oracle Data Integration offer robust and reliable solutions for key technology trends including cloud computing, big data analytics, real-time business intelligence and continuous data availability. Via the tight integration with Oracle’s database, middleware, and application offerings Oracle Data Integration will continue to support the new features and capabilities right away as these products evolve and provide advance features. E    Extreme Performance: Both GoldenGate and Data Integrator are known for their high performance. The new release widens the gap even further against competition. Oracle GoldenGate 12c’s Integrated Delivery feature enables higher throughput via a special application programming interface into Oracle Database. As mentioned in the press release, customers already report up to 5X higher performance compared to earlier versions of GoldenGate. Oracle Data Integrator 12c introduces parallelism that significantly increases its performance as well. Fast Time-to-Value via Higher IT Productivity and Simplified Solutions:  Oracle Data Integrator 12c’s new flow-based declarative UI brings superior developer productivity, ease of use, and ultimately fast time to market for end users.  It also gives the ability to seamlessly reuse mapping logic speeds development.Oracle GoldenGate 12c ‘s Integrated Delivery feature automatically optimally tunes the process, saving time while improving performance. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. On November 12th we will reveal much more about the new release in our video webcast "Introducing 12c for Oracle Data Integration". Our customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Please join us at this free event to learn more from our executives about the 12c release, hear our customers’ perspectives on the new features, and ask your questions to our experts in the live Q&A. Also, please continue to follow our blogs, tweets, and Facebook updates as we unveil more about the new features of the latest release. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • Big Data – Final Wrap and What Next – Day 21 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored various resources related to learning Big Data and in this blog post we will wrap up this 21 day series on Big Data. I have been exploring various terms and technology related to Big Data this entire month. It was indeed fun to write about Big Data in 21 days but the subject of Big Data is much bigger and larger than someone can cover it in 21 days. My first goal was to write about the basics and I think we have got that one covered pretty well. During this 21 days I have received many questions and answers related to Big Data. I have covered a few of the questions in this series and a few more I will be covering in the next coming months. Now after understanding Big Data basics. I am personally going to do a list of the things next. I thought I will share the same with you as this will give you a good idea how to continue the journey of the Big Data. Build a schedule to read various Apache documentations Watch all Pluralsight Courses Explore HortonWorks Sandbox Start building presentation about Big Data – this is a great way to learn something new Present in User Groups Meetings on Big Data Topics Write more blog posts about Big Data I am going to continue learning about Big Data – I want you to continue learning Big Data. Please leave a comment how you are going to continue learning about Big Data. I will publish all the informative comments on this blog with due credit. I want to end this series with the infographic by UMUC. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Partner Webcast - Focus on Oracle Data Profiling and Data Quality 11g

    - by lukasz.romaszewski(at)oracle.com
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:RO;} Partner Webcast Focus on Oracle Data Profiling and Data Quality 11g February 24th, 12am  CET   Oracle offers an integrated suite Data Quality software architected to discover and correct today's data quality problems and establish a platform prepared for tomorrow's yet unknown data challenges. Oracle Data Profiling provides data investigation, discovery, and profiling in support of quality, migration, integration, stewardship, and governance initiatives. It includes a broad range of features that expand upon basic profiling, including automated monitoring, business-rule validation, and trend analysis. Oracle Data Quality for Data Integrator provides cleansing, standardization, matching, address validation, location enrichment, and linking functions for global customer data and operational business data. It ensures that data adheres to established standards that are adaptable to fit each organization's specific needs.  Both single - and double - byte data are processed in local languages to provide a unique and centralized view of customers, products and services.   During this in-person briefing, Data Integration Solution Specialists will be providing a technical overview and a walkthrough.   Agenda ·         Oracle Data Integration Strategy overview ·         A focus on Oracle Data Profiling and Oracle Data Quality for Data Integrator: o   Oracle Data Profiling o   Oracle Data Quality for Data Integrator o   Live demoo   Q&A Delivery Format  This FREE online LIVE eSeminar will be delivered over the Web and Conference Call. Registrations   received less than 24hours  prior to start time may not receive confirmation to attend. To register , click here. For any questions please contact [email protected]. 

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  • Google Visualization legend format Jquery ui tabs

    - by Liam
    I've got an issue when using the Google Visualization api line chart with Jquery ui tabs. I've got two graphs on two tabs. The first graph, which is visible by default displays fine: the second chart on the hidden tab seems to be messing up the key: I've tried changing the options but nothing I do seems to make any difference. Here are my options: options = { 'title': title, titleTextStyle: { color: color, fontSize: 20 }, 'width': 950, 'height': 400, hAxis: { minorGridlines: { count: x } }, chartArea: { width: 880 }, legend: { position: 'bottom', textStyle: { fontSize: 10 } } }; // Instantiate and draw our chart, passing in some options. var chart = new google.visualization.LineChart(document.getElementById(divId)); chart.draw(data, options); $('#tabs').tabs(); any thoughts on what is causing this and how to prevent it?? Edit If I remove the tabs() call it displays correctly. As per answer below from @Vipul tried setting the div to a fixed width, no difference.

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  • Google Visualization Annotated Time Line, removing data points.

    - by Vitaly Babiy
    I am trying to build a graph that will change resolution depending on how far you are zoomed in. Here is what it looks like when you are complete zoomed out. So this looks good so when I zoom in I get a higher resolution data and my graph looks like this: The problem is when I zoom out the higher resolution data does not get cleared out of the graph: The tables below the graphs are table display what is in the DataTable. This is what drawing code looks like. var g_graph = new google.visualization.AnnotatedTimeLine(document.getElementById('graph_div_json')); var table = new google.visualization.Table(document.getElementById('table_div_json')); function handleQueryResponse(response){ log("Drawing graph") var data = response.getDataTable() g_graph.draw(data, {allowRedraw:true, thickness:2, fill:50, scaleType:'maximized'}) table.draw(data, {allowRedraw:true}) } I am try to find a way for it to only displaying the data that is in the DataTable. I have tried removing the allowRedraw flag but then it breaks the zooming operation. Any help would be greatly appreciated. Thanks

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  • Big Data – Buzz Words: What is HDFS – Day 8 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Oracle Data Integration 12c: Perspectives of Industry Experts, Customers and Partners

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 As you may have seen from our recent blog posts on Oracle Data Integrator 12c and Oracle GoldenGate 12c, we are very excited to share with you the great new features the 12c release brings to Oracle’s data integration solutions. And, fortunately we are not alone in this sentiment. Since the press announcement October 17th, which incorporates our customers' and experts' testimonials, we have seen positive comments in leading technology publications and social media as well. Here are some examples: In CIO and PCWorld you can find Joab Jackson’s article, Oracle Data Integrator 12c ready for real-time analysis, where wrote about the tight integration between Oracle Data Integrator and Oracle GoldenGate . He noted “Heeding the call from enterprise customers who clamor for more immediacy in their data-driven reports, Oracle has updated its data-integration software portfolio so that it can more rapidly deliver data to data warehouses and analysis applications.” Integration Developer News’ Vance McCarthy wrote the article Oracle Ships ‘Future Proofs’ Integration Tools for Traditional, Cloud, Big Data, Real-Time Projects and mentioned that “Oracle Data Integrator 12c and Oracle GoldenGate 12c sport a wide range of improvements to let devs more easily deliver data integration for cloud, analytics, big data and other new projects that leverage multiple datasets for business.“ InformationWeek’s Doug Henschen gave a great overview to several key features including the new flow-based UI in Oracle Data Integrator. Doug said “Oracle Data Integrator 12c introduces a complete makeover of the job-building experience, while real-time oriented GoldenGate 12c introduces performance gains “. In Database Trends and Applications’ article Oracle Strengthens Data Integration with Release of Oracle Data Integrator 12c and Oracle GoldenGate 12c highlighted the productivity aspect of the new solution with his remarks: “tight integration between Oracle Data Integrator 12c and Oracle GoldenGate 12c enables developers to leverage Oracle GoldenGate’s low overhead, real-time change data capture completely within the Oracle Data Integrator Studio without additional training”. We are also thrilled about what our customers and partners have to say about our products and the new release. And we are equally excited to share those perspectives with you in our upcoming launch video webcast on November 12th. SolarWorld Industries America’s Senior Database Manager, Russ Toyama will join our executives in our studio in Redwood Shores to discuss GoldenGate’s core benefits and the new release, while Surren Partharb, CTO of Strategic Technology Services for BT, and Mark Rittman, CTO of Rittman Mead, will provide their comments via the interviews conducted in the UK. This interactive panel discussion in the video webcast will unveil the new release with the expertise of our development executives and the great insight from our customers and partners. In addition, our product experts will be available online to answer chat questions. This is really a great opportunity to learn how Oracle's data integration offering has changed the integration and replication technology space with the new release, and established itself as the new leader. If you have not registered for this free event yet, you can do so via this link. We will run the live event at 8am PT/4pm GMT, followed by a replay of the event with live chat for Q&A  at 10am PT/6pm GMT. The replay will be available on-demand for those who register but cannot attend either session on November 12th. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";}

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  • What is (are) the most useful technique/visualization for overall project status?

    - by Wayne Werner
    For reasons "above my pay grade", we're developing an issue/project tracking system where I work (similar to Trac, FogBugz, etc). The managers want a useful tool to be able to track the overall health of the project (e.g. How much time left, how are we performing vs estimates) and one of the features that has been requested is some type of critical path support and visualization. The logic explained to me is that they want to be sure that at least the most important pieces of the project are currently being worked on. The initial idea was that we would create task-based dependencies. My understanding of project management tells me that this kind of granular approach is unnecessary - having milestones with specific deadlines/dependencies is much more useful. I would like to know what are the most useful techniques and "pretty pictures" you've seen/used for project development. Having objective data would be best, but somewhat subjective data is helpful too.

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  • Mass Transit Visualizations Reveal Cities’ Daily Movements [Video]

    - by Jason Fitzpatrick
    If you’re a sucker for data visualization–and we certainly are–this collection of mass transit data visualized over city maps are fascinating and makes mass transit look like a cell culture unfolding. Check out one day in the life of the New York City mass transit system in the video above and then hit up the link below to check out other cities including Chicago, Washington D.C., Boston, and Manchester. Mesmerizing Visualizations Show Mass-Transit Patterns of Major Cities [Wired] HTG Explains: Does Your Android Phone Need an Antivirus? How To Use USB Drives With the Nexus 7 and Other Android Devices Why Does 64-Bit Windows Need a Separate “Program Files (x86)” Folder?

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  • android application that visualize real time data

    - by matarsak
    I want to build and android app that visualize real time data , I set up a UDP channel that get the data , now I want to visualize it . I now that I can use openGL ES. but I have now back ground and in a few weeks I dont think that i'm able to learn that . what about android processing ? could it be used for extensive visualization task like this? or it's limited ? I heard it's not hard to learn that. any other option ?

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  • New Big Data Appliance Security Features

    - by mgubar
    The Oracle Big Data Appliance (BDA) is an engineered system for big data processing.  It greatly simplifies the deployment of an optimized Hadoop Cluster – whether that cluster is used for batch or real-time processing.  The vast majority of BDA customers are integrating the appliance with their Oracle Databases and they have certain expectations – especially around security.  Oracle Database customers have benefited from a rich set of security features:  encryption, redaction, data masking, database firewall, label based access control – and much, much more.  They want similar capabilities with their Hadoop cluster.    Unfortunately, Hadoop wasn’t developed with security in mind.  By default, a Hadoop cluster is insecure – the antithesis of an Oracle Database.  Some critical security features have been implemented – but even those capabilities are arduous to setup and configure.  Oracle believes that a key element of an optimized appliance is that its data should be secure.  Therefore, by default the BDA delivers the “AAA of security”: authentication, authorization and auditing. Security Starts at Authentication A successful security strategy is predicated on strong authentication – for both users and software services.  Consider the default configuration for a newly installed Oracle Database; it’s been a long time since you had a legitimate chance at accessing the database using the credentials “system/manager” or “scott/tiger”.  The default Oracle Database policy is to lock accounts thereby restricting access; administrators must consciously grant access to users. Default Authentication in Hadoop By default, a Hadoop cluster fails the authentication test. For example, it is easy for a malicious user to masquerade as any other user on the system.  Consider the following scenario that illustrates how a user can access any data on a Hadoop cluster by masquerading as a more privileged user.  In our scenario, the Hadoop cluster contains sensitive salary information in the file /user/hrdata/salaries.txt.  When logged in as the hr user, you can see the following files.  Notice, we’re using the Hadoop command line utilities for accessing the data: $ hadoop fs -ls /user/hrdataFound 1 items-rw-r--r--   1 oracle supergroup         70 2013-10-31 10:38 /user/hrdata/salaries.txt$ hadoop fs -cat /user/hrdata/salaries.txtTom Brady,11000000Tom Hanks,5000000Bob Smith,250000Oprah,300000000 User DrEvil has access to the cluster – and can see that there is an interesting folder called “hrdata”.  $ hadoop fs -ls /user Found 1 items drwx------   - hr supergroup          0 2013-10-31 10:38 /user/hrdata However, DrEvil cannot view the contents of the folder due to lack of access privileges: $ hadoop fs -ls /user/hrdata ls: Permission denied: user=drevil, access=READ_EXECUTE, inode="/user/hrdata":oracle:supergroup:drwx------ Accessing this data will not be a problem for DrEvil. He knows that the hr user owns the data by looking at the folder’s ACLs. To overcome this challenge, he will simply masquerade as the hr user. On his local machine, he adds the hr user, assigns that user a password, and then accesses the data on the Hadoop cluster: $ sudo useradd hr $ sudo passwd $ su hr $ hadoop fs -cat /user/hrdata/salaries.txt Tom Brady,11000000 Tom Hanks,5000000 Bob Smith,250000 Oprah,300000000 Hadoop has not authenticated the user; it trusts that the identity that has been presented is indeed the hr user. Therefore, sensitive data has been easily compromised. Clearly, the default security policy is inappropriate and dangerous to many organizations storing critical data in HDFS. Big Data Appliance Provides Secure Authentication The BDA provides secure authentication to the Hadoop cluster by default – preventing the type of masquerading described above. It accomplishes this thru Kerberos integration. Figure 1: Kerberos Integration The Key Distribution Center (KDC) is a server that has two components: an authentication server and a ticket granting service. The authentication server validates the identity of the user and service. Once authenticated, a client must request a ticket from the ticket granting service – allowing it to access the BDA’s NameNode, JobTracker, etc. At installation, you simply point the BDA to an external KDC or automatically install a highly available KDC on the BDA itself. Kerberos will then provide strong authentication for not just the end user – but also for important Hadoop services running on the appliance. You can now guarantee that users are who they claim to be – and rogue services (like fake data nodes) are not added to the system. It is common for organizations to want to leverage existing LDAP servers for common user and group management. Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. This simplifies the deployment and administration of the secure environment. Authorize Access to Sensitive Data Kerberos-based authentication ensures secure access to the system and the establishment of a trusted identity – a prerequisite for any authorization scheme. Once this identity is established, you need to authorize access to the data. HDFS will authorize access to files using ACLs with the authorization specification applied using classic Linux-style commands like chmod and chown (e.g. hadoop fs -chown oracle:oracle /user/hrdata changes the ownership of the /user/hrdata folder to oracle). Authorization is applied at the user or group level – utilizing group membership found in the Linux environment (i.e. /etc/group) or in the LDAP server. For SQL-based data stores – like Hive and Impala – finer grained access control is required. Access to databases, tables, columns, etc. must be controlled. And, you want to leverage roles to facilitate administration. Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project’s founding members. Sentry satisfies the following three authorization requirements: Secure Authorization:  the ability to control access to data and/or privileges on data for authenticated users. Fine-Grained Authorization:  the ability to give users access to a subset of the data (e.g. column) in a database Role-Based Authorization:  the ability to create/apply template-based privileges based on functional roles. With Sentry, “all”, “select” or “insert” privileges are granted to an object. The descendants of that object automatically inherit that privilege. A collection of privileges across many objects may be aggregated into a role – and users/groups are then assigned that role. This leads to simplified administration of security across the system. Figure 2: Object Hierarchy – granting a privilege on the database object will be inherited by its tables and views. Sentry is currently used by both Hive and Impala – but it is a framework that other data sources can leverage when offering fine-grained authorization. For example, one can expect Sentry to deliver authorization capabilities to Cloudera Search in the near future. Audit Hadoop Cluster Activity Auditing is a critical component to a secure system and is oftentimes required for SOX, PCI and other regulations. The BDA integrates with Oracle Audit Vault and Database Firewall – tracking different types of activity taking place on the cluster: Figure 3: Monitored Hadoop services. At the lowest level, every operation that accesses data in HDFS is captured. The HDFS audit log identifies the user who accessed the file, the time that file was accessed, the type of access (read, write, delete, list, etc.) and whether or not that file access was successful. The other auditing features include: MapReduce:  correlate the MapReduce job that accessed the file Oozie:  describes who ran what as part of a workflow Hive:  captures changes were made to the Hive metadata The audit data is captured in the Audit Vault Server – which integrates audit activity from a variety of sources, adding databases (Oracle, DB2, SQL Server) and operating systems to activity from the BDA. Figure 4: Consolidated audit data across the enterprise.  Once the data is in the Audit Vault server, you can leverage a rich set of prebuilt and custom reports to monitor all the activity in the enterprise. In addition, alerts may be defined to trigger violations of audit policies. Conclusion Security cannot be considered an afterthought in big data deployments. Across most organizations, Hadoop is managing sensitive data that must be protected; it is not simply crunching publicly available information used for search applications. The BDA provides a strong security foundation – ensuring users are only allowed to view authorized data and that data access is audited in a consolidated framework.

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  • How to present a stable data model in a public API that allows internal data structures to be changed without breaking the public view of the data?

    - by Max Palmer
    I am in the process of developing an application that allows users to write C# scripts. These scripts allow users to call selected methods and to access and manipulate data in a document. This works well, however, in the development version, scripts access the document's (internal) data structures directly. This means that if we were to change the internal data model/structure, there is a good chance that someone's script will no longer compile. We obviously want to prevent this breaking change from happening, but still want to allow the user to write sensible C# code (whilst not restricting how we develop our internal data model as a result). We therefore need to decouple our scripting API and its data structures from our internal methods and data structures. We've a few ideas as to how we might allow the user to access a what is effectively a stable public version of the document's internal data*, but I wanted to throw the question out there to someone who might have some real experience of this problem. NB our internal document's data structure is quite complex and it could be quite difficult to wrap. We know we want to expose as little as possible in our public API, especially as once it's out there, it's out there for good. Can anyone help? How do scripting languages / APIs decouple their public API and data structures from their internal data structures? Is there no real alternative to having to write a complex interaction layer? If we need to do this, what's a good approach or pattern for wrapping complex data structures that include nested objects, including collections? I've looked at the API facade pattern, which looks like it's trying to address these kinds of issues, but are there alternatives? *One idea is to build a data facade that is kept stable across versions of our application. The facade exposes a set of facade data objects that are used in the script code. These maintain backwards compatibility and wrap access to our internal document's data model.

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