Big Data Sea

Like any worthwhile business endeavor, improving the quality and utility of your data is a multi-step, multi-method process. Here's how: Method 1:  Big data scripting takes a huge volume of data and uses a scripting language that can communicate and combine with other existing languages to clean and process the data for analysis.     Errors in judgment and execution can trip up the whole process. Method 2:  Traditional ETL (extract, load, transform) tools integrate data from various sources and load it into a data warehouse where it's then prepped for analysis. But it usually requires a team of skilled, in-house data scientists to manually scrub the data first in order to address any incompatibilities with schema  and formats. Even less convenient is that these tools often process in batches instead of in real-time. Traditional ETL requires the type of infrastructure, on-site expertise, and time commitment that few or

Data quality practices from BI and data warehousing are geared towards data cleansing to improve data correctness and data integrity which is used for reporting purposes. Correctness is difficult to determine when using data from external sources, and structural integrity can be difficult to test with unstructured and differently structured (non-relational) data. As the volume, sources, and velocity of data creation increase, businesses are grappling with the reality of figuring out what to do with it all and how to do it. And if your business hasn't determined the most real way to use its own data, then you're missing out on critical opportunities to transform your business and gain a significant advantage. Of course, without good data, it's a heck of a lot harder to do what you want to do. Whether you're launching a new product or service, or simply responding to the moves of your biggest competitor, making smart, timely business decisions depends almost entirely on

Big data architecture is the predominant system used to ingest and process large amounts of data (often referred to as " big data ") so it can be analysed for business purposes.  Big data architecture is designed to handle the following types of work: Batch processing of big data sources. Real-time big data ingestion. Predictive analytics and machine learning. How Big Data Architecture Benefits Companies? The volume of data growing daily in exponential form and is available for analysis. And, there are number streaming sources, including the data from traffic sensors, health sector’s sensors, transaction logs, and activity logs. Using a big data architecture can help your business save money and make critical decisions, including: Reducing costs . Big data technologies such as Hadoop and cloud-based analytics can significantly reduce costs when it comes to storing large amounts of data. Making faster, better decisions . Using th

Big Data The term “big data” refers to data that is so large, fast or so complex to handle it with traditional methods. The act of storing large amounts of data for analytics has been around a long time. But the concept of big data gained momentum in the early 2000s when industry analyst  Doug Laney  articulated the now-mainstream definition of big data as the three V’s: Volume : Organizations collect data from a variety of sources, including business transactions, IoT /smart devices, smart equipment, videos, social media and more. Storing this type of data would have been problem, but cheaper storage on platforms like data lakes and Hadoop have eased the burden. Velocity : With the growth in the data streams and Internet of Things in to businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time. Variety : Data comes in all types of formats – from structur