Big Data: what or who is the data scientist?


As described in an earlier post here I outlined the fact that becoming a data scientist requires a lot of knowledge. Focusing back, a data scientist needs to have knowledge in different IT domains: General understanding of distributed systems and how they work. This includes administration skills for Linux as well as hardware related skills such as networking. Knowledge in Hadoop or similar technologies. This knowledge basically builds on top of the former one but it is sort of different and requires a more software focused knowledge. Great statistical/mathematical knowledge. This is necessary to actually work on the required tasks and to figure out how they can be applied to real algorithms. Presentation skills. All is worth nothing as long as someone can’t represent the data or things found in the data. The management might not see the points if the person can’t present data in an appropriate way. In addition, there are some other skills necessary: Knowledge of the

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Big Data challenges: moving data for analysis


Another issue with Big Data is indicated by (Alexander, Hoisie , & Szalay , 2011). The problem is that Data can’t be moved easily for analysis. With Big Data, we often have some Terabytes or more. Moving this via a network connection is not that easy or even impossible. If real-time data is analyzed, it is literally impossible to move that amount of data to another cluster, since the data will be incorrect or not available at this time. Real-Time data analysis is also necessary in fraud protection. If this data now has to be moved to another cluster, it might already be too late. In traditional databases, this wasn’t that hard since the data was often some Gigabyte in a single database. With Big Data, data is in various formats, at high volume and at high velocity. To comply with all these factors and moving data to another cluster, this might not be possible. (Alexander, Hoisie , & Szalay

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Big Data challenges: data partitioning and concurrency


Data needs to be partitioned if it can’t be stored on a single system. With Big Data applications, we don’t talk about small storages but rather about distributed systems. Data might be partitioned over hundred or thousand of nodes and the database must scale out to that demand. Data partitioning is a key concept for databases and it serves as well in Big Data applications. However, if data is distributed over some servers, it might take a while until all nodes are informed about the changes. To avoid concurrency issues, the data must be locked. This might result in a poor database performance if the database should be kept consistent at all time. One solution is to forget about data consistency in favor of data partitioning. This approach is described in detail in section 1.6.2 when we will focus on the CAP-Theorem. Let’s imagine a Web shop. There are 2 users in our sample; both of them (let’s call them

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Big Data challenges: different storage systems


A main factor to Big Data is the variety of data. Data may not only change over time (e.g. a web shop not only wants to sell books but also cars) but will also have different formats. Databases must provide this possibility. Companies might not only store all their data in one single database but rather in different databases and different APIs consume different formats such as JSON, XML or any other type. Facebook, for instance, uses MySQL, Cassandra and HBase to store their data. They have three different storage systems (Harris, 2011) (Muthukkaruppan, 2010), each of them serving a different need. (Helland, 2011) described the challenges for datastores with 4 key principles: unlocked data inconsistent schema extract, transform and load too much to be accurate By unlocked data, it is meant that data is usually locked but with Big Data, this might result in problems, as they don’t rely on locked data. On the other hand, unlocked data leads

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Big Data challenges: storage performance


Big Data needs Big Storage and storage is at the end a physical device. Until now, most storage devices are hard disks that require mechanical movements. A common hard drive available today (December 2012) has 15,000 (Seagate, 2013) revolutions per minute (rpm) and a desktop hard drive has some 7,200-rpm. In any case, this means that there is significant latency involved until the reading head is in place. The mechanical approach to storage has been around for decades and scientists as well as engineers complain about storage performance. In-memory was always faster than hard disk storage and the network speed is higher than what can be done with hard disks. (Anthes, 2012) states that disk based storage is about 10-100 times slower than a network and about 1,000 times slower than main memory. This means that there is a significant “bottleneck” when it comes to delivering data from a disk-based storage to an application. As big data is about storing

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